{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kaggle - Categorical Feature Encoding Challenge II\n",
    "\n",
    "### Binary classification, with every feature a categorical (and interactions!)\n",
    "\n",
    "[https://www.kaggle.com/c/cat-in-the-dat-ii](https://www.kaggle.com/c/cat-in-the-dat-ii)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1st Place Solution\n",
    "\n",
    "Congrats to all the participants in this great and challenging tabular competition! Thank you  Kaggle for organizing this competition.\n",
    "\n",
    "My solution is very simple, NN plays a key role. The categorical features, especially with high cardinality are very suitable for neural network to exert its power.\n",
    "\n",
    "### About models: \n",
    "4x NN,  1x Catboost, and blend of some of public kernels.  All NN models on same features,  and one of them resulted in 0.78672 on the public LB. Thanks to @sergey and @siavash for sharing the public kernels, I used them in blending.\n",
    "\n",
    "### Feature engineering for NN models:\n",
    "\n",
    "1. All features are converted into category type then Label Encoding\n",
    "2. Ordinal Encoding: (ord_1 ~ ord_5) -> (ord_1_en ~ ord_5_en)\n",
    "\n",
    "That’s all.\n",
    "\n",
    "### NN Models\n",
    "In our solution, we used several state-of-the-art models for CTR prediction, including CIN in xDeepFM, PNN, Cross in DCN, AutoInt, etc.\n",
    "\n",
    "1. Linear+DNN+CIN (0.78672 public LB)\n",
    "2. FM+Cross+PNN (0.78655 public LB)\n",
    "3. FM+DCN+DNN (0.78652 public LB)\n",
    "4. Linear+DNN+AutoInt (0.78665 public LB)\n",
    "\n",
    "There are many components available for feature extraction on tabular data and they can be combined with various ways. It is a huge workload to trail by coding from scratch every time. **Deeptables** greatly simplifies this job with only a few lines of code. \n",
    "\n",
    "[https://github.com/DataCanvasIO/deeptables](https://github.com/DataCanvasIO/deeptables)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from deeptables.models.deeptable import DeepTable, ModelConfig\n",
    "from tensorflow.keras.utils import plot_model\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading data\n",
    "\n",
    "Please download datasets from https://www.kaggle.com/c/cat-in-the-dat-ii/data to the local directory `cat-in-the-dat-ii` by yourself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 10.4 s, sys: 1.2 s, total: 11.6 s\n",
      "Wall time: 3.69 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "data_path = 'cat-in-the-dat-ii'\n",
    "train = pd.read_csv(f'{data_path}/train.csv')\n",
    "test = pd.read_csv(f'{data_path}/test.csv')\n",
    "submission = pd.read_csv(f'{data_path}/sample_submission.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ordinal Encoding: ord_1 ~ ord_5 -> ord_1_en ~ ord_5_en"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 24.5 s, sys: 2.53 s, total: 27.1 s\n",
      "Wall time: 1.55 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "ord_order = [\n",
    "    [1.0, 2.0, 3.0],\n",
    "    ['Novice', 'Contributor', 'Expert', 'Master', 'Grandmaster'],\n",
    "    ['Freezing', 'Cold', 'Warm', 'Hot', 'Boiling Hot', 'Lava Hot']\n",
    "]\n",
    "\n",
    "for i in range(1, 3):\n",
    "    ord_order_dict = {i : j for j, i in enumerate(ord_order[i])}\n",
    "    train[f'ord_{i}_en'] = train[f'ord_{i}'].fillna('NULL').map(ord_order_dict)\n",
    "    test[f'ord_{i}_en'] = test[f'ord_{i}'].fillna('NULL').map(ord_order_dict)\n",
    "    \n",
    "for i in range(3, 6):\n",
    "    ord_order_dict = {i : j for j, i in enumerate(sorted(list(set(list(train[f'ord_{i}'].dropna().unique()) + list(test[f'ord_{i}'].dropna().unique())))))}\n",
    "    train[f'ord_{i}_en'] = train[f'ord_{i}'].fillna('NULL').map(ord_order_dict)\n",
    "    test[f'ord_{i}_en'] = test[f'ord_{i}'].fillna('NULL').map(ord_order_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert into `categroy`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "\n",
    "cat_cols = [c for c in train.columns if '_en' not in c and c != 'target']\n",
    "train[cat_cols] = train[cat_cols].astype('category')\n",
    "test[cat_cols] = test[cat_cols].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X shape: (600000, 28), y shape: (600000,), X_test shape: (400000, 28)\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "y = train['target']\n",
    "X = train\n",
    "X.drop(['target','id'], axis=1, inplace=True)\n",
    "\n",
    "X_test = test\n",
    "X_test.drop(['id'], axis=1, inplace=True)\n",
    "print(f'X shape: {X.shape}, y shape: {y.shape}, X_test shape: {X_test.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_folds=3 #for faster demo, in the competition is 50\n",
    "epochs=1 #for faster demo, in the competition is 100\n",
    "batch_size=128"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear+DNN+CIN - a varient of xDeepFM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start cross validation\n",
      "2 class detected, {0, 1}, so inferred as a [binary classification] task\n",
      "Preparing features cost:0.19865846633911133\n",
      "Imputation cost:6.917970657348633\n",
      "Categorical encoding cost:16.448770761489868\n",
      "fit_transform cost:23.712860822677612\n",
      "transform X_test\n",
      "transform_X cost:176.98967289924622\n",
      "Iterators:KFold(n_splits=3, random_state=9527, shuffle=True)\n",
      "Injected a callback [EarlyStopping]. monitor:val_AUC, patience:1, mode:max\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Fold:1\n",
      "\n",
      "1 Physical GPUs, 1 Logical GPUs\n",
      ">>>>>>>>>>>>>>>>>>>>>> Model Desc <<<<<<<<<<<<<<<<<<<<<<< \n",
      "---------------------------------------------------------\n",
      "inputs:\n",
      "---------------------------------------------------------\n",
      "['all_categorical_vars: (23)', 'input_continuous_all: (5)']\n",
      "---------------------------------------------------------\n",
      "embeddings:\n",
      "---------------------------------------------------------\n",
      "input_dims: [4, 4, 4, 4, 4, 5, 8, 8, 8, 6, 1222, 1521, 224, 224, 2220, 5, 7, 8, 17, 28, 192, 9, 14]\n",
      "output_dims: [20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
      "dropout: 0.3\n",
      "---------------------------------------------------------\n",
      "dense: dropout: 0\n",
      "batch_normalization: False\n",
      "---------------------------------------------------------\n",
      "concat_embed_dense: shape: (None, 465)\n",
      "---------------------------------------------------------\n",
      "nets: ['linear', 'dnn_nets', 'cin_nets']\n",
      "---------------------------------------------------------\n",
      "linear: input_shape (None, 28), output_shape (None, 1)\n",
      "dnn: input_shape (None, 465), output_shape (None, 300)\n",
      "cin: input_shape (None, 23, 20), output_shape (None, 1)\n",
      "---------------------------------------------------------\n",
      "stacking_op: add\n",
      "---------------------------------------------------------\n",
      "output: activation: sigmoid, output_shape: (None, 1), use_bias: False\n",
      "loss: binary_crossentropy\n",
      "optimizer: Adam\n",
      "---------------------------------------------------------\n",
      "\n",
      "Train on 400000 samples, validate on 200000 samples\n",
      "400000/400000 [==============================] - 72s 180us/sample - loss: 0.6810 - AUC: 0.6925 - accuracy: 0.7909 - val_loss: 0.4054 - val_AUC: 0.7816 - val_accuracy: 0.8208\n",
      "Fold 1 fitting over.\n",
      "Fold 1 scoring over.\n",
      "Save model to:dt_output/dt_20200324 154910_linear_dnn_nets_cin_nets/linear_dnn_nets_cin_nets-kfold-1.h5.\n",
      "\n",
      "Fold:2\n",
      "\n",
      "1 Physical GPUs, 1 Logical GPUs\n",
      ">>>>>>>>>>>>>>>>>>>>>> Model Desc <<<<<<<<<<<<<<<<<<<<<<< \n",
      "---------------------------------------------------------\n",
      "inputs:\n",
      "---------------------------------------------------------\n",
      "['all_categorical_vars: (23)', 'input_continuous_all: (5)']\n",
      "---------------------------------------------------------\n",
      "embeddings:\n",
      "---------------------------------------------------------\n",
      "input_dims: [4, 4, 4, 4, 4, 5, 8, 8, 8, 6, 1222, 1521, 224, 224, 2220, 5, 7, 8, 17, 28, 192, 9, 14]\n",
      "output_dims: [20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
      "dropout: 0.3\n",
      "---------------------------------------------------------\n",
      "dense: dropout: 0\n",
      "batch_normalization: False\n",
      "---------------------------------------------------------\n",
      "concat_embed_dense: shape: (None, 465)\n",
      "---------------------------------------------------------\n",
      "nets: ['linear', 'dnn_nets', 'cin_nets']\n",
      "---------------------------------------------------------\n",
      "linear: input_shape (None, 28), output_shape (None, 1)\n",
      "dnn: input_shape (None, 465), output_shape (None, 300)\n",
      "cin: input_shape (None, 23, 20), output_shape (None, 1)\n",
      "---------------------------------------------------------\n",
      "stacking_op: add\n",
      "---------------------------------------------------------\n",
      "output: activation: sigmoid, output_shape: (None, 1), use_bias: False\n",
      "loss: binary_crossentropy\n",
      "optimizer: Adam\n",
      "---------------------------------------------------------\n",
      "\n",
      "Train on 400000 samples, validate on 200000 samples\n",
      "400000/400000 [==============================] - 70s 175us/sample - loss: 0.4137 - AUC: 0.7610 - accuracy: 0.8189 - val_loss: 0.3996 - val_AUC: 0.7832 - val_accuracy: 0.8244\n",
      "Fold 2 fitting over.\n",
      "Fold 2 scoring over.\n",
      "Save model to:dt_output/dt_20200324 154910_linear_dnn_nets_cin_nets/linear_dnn_nets_cin_nets-kfold-2.h5.\n",
      "\n",
      "Fold:3\n",
      "\n",
      "1 Physical GPUs, 1 Logical GPUs\n",
      ">>>>>>>>>>>>>>>>>>>>>> Model Desc <<<<<<<<<<<<<<<<<<<<<<< \n",
      "---------------------------------------------------------\n",
      "inputs:\n",
      "---------------------------------------------------------\n",
      "['all_categorical_vars: (23)', 'input_continuous_all: (5)']\n",
      "---------------------------------------------------------\n",
      "embeddings:\n",
      "---------------------------------------------------------\n",
      "input_dims: [4, 4, 4, 4, 4, 5, 8, 8, 8, 6, 1222, 1521, 224, 224, 2220, 5, 7, 8, 17, 28, 192, 9, 14]\n",
      "output_dims: [20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
      "dropout: 0.3\n",
      "---------------------------------------------------------\n",
      "dense: dropout: 0\n",
      "batch_normalization: False\n",
      "---------------------------------------------------------\n",
      "concat_embed_dense: shape: (None, 465)\n",
      "---------------------------------------------------------\n",
      "nets: ['linear', 'dnn_nets', 'cin_nets']\n",
      "---------------------------------------------------------\n",
      "linear: input_shape (None, 28), output_shape (None, 1)\n",
      "dnn: input_shape (None, 465), output_shape (None, 300)\n",
      "cin: input_shape (None, 23, 20), output_shape (None, 1)\n",
      "---------------------------------------------------------\n",
      "stacking_op: add\n",
      "---------------------------------------------------------\n",
      "output: activation: sigmoid, output_shape: (None, 1), use_bias: False\n",
      "loss: binary_crossentropy\n",
      "optimizer: Adam\n",
      "---------------------------------------------------------\n",
      "\n",
      "Train on 400000 samples, validate on 200000 samples\n",
      "400000/400000 [==============================] - 70s 175us/sample - loss: 0.4444 - AUC: 0.7428 - accuracy: 0.8124 - val_loss: 0.4009 - val_AUC: 0.7819 - val_accuracy: 0.8240\n",
      "Fold 3 fitting over.\n",
      "Fold 3 scoring over.\n",
      "Save model to:dt_output/dt_20200324 154910_linear_dnn_nets_cin_nets/linear_dnn_nets_cin_nets-kfold-3.h5.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:  5.4min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fit_cross_validation cost:523.647552728653\n",
      "CPU times: user 17min 46s, sys: 2min 21s, total: 20min 8s\n",
      "Wall time: 8min 43s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "conf = ModelConfig(\n",
    "    dnn_params={\n",
    "        'hidden_units':((300, 0.3, True),(300, 0.3, True),), #hidden_units\n",
    "        'dnn_activation':'relu',\n",
    "    },\n",
    "    fixed_embedding_dim=True,\n",
    "    embeddings_output_dim=20,\n",
    "    nets =['linear','cin_nets','dnn_nets'],\n",
    "    stacking_op = 'add',\n",
    "    output_use_bias = False,\n",
    "    cin_params={\n",
    "       'cross_layer_size': (200, 200),\n",
    "       'activation': 'relu',\n",
    "       'use_residual': False,\n",
    "       'use_bias': True,\n",
    "       'direct': True, \n",
    "       'reduce_D': False,\n",
    "    },\n",
    ")\n",
    "\n",
    "dt = DeepTable(config = conf)\n",
    "oof_proba, eval_proba, test_prob = dt.fit_cross_validation(\n",
    "    X, y, X_eval=None, X_test=X_test, \n",
    "    num_folds=n_folds, stratified=False, iterators=None, \n",
    "    batch_size=batch_size, epochs=epochs, verbose=1, callbacks=[], n_jobs=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Output submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission['target'] = test_prob\n",
    "submission.to_csv(f'submission_linear_dnn_cin_kfold50.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load model from disk:dt_output/dt_20200324 154910_linear_dnn_nets_cin_nets/linear_dnn_nets_cin_nets-kfold-3.h5.\n",
      "1 Physical GPUs, 1 Logical GPUs\n"
     ]
    },
    {
     "data": {
      "image/png": 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s6CNLtXsC0NfXb/DtNDFqgwYMGGBtbX3w4MGoqKjZs2cTQsRqWUlJKTIy8ujRo4QQW1tb+jtxfaSVWYQ5c+aoq6tHR0dv2LDBy8urOSYhoK+v379/f/omvPU9pymLvsVYW1t37tw5Nze3srKyKe0INr36NhD6cBNdYdYgYpRY6twAm9gmgGiopgAAgBnGxsYURfn6+gqGvH79es+ePU1s9t27d5WVlc7OziKeY2JiQgjx8/MT/MqelJR04sSJe/fuXb16ddSoUfRA+qAQ/b+CggKXy20weVlZ2YsXL5KTk/39/Q8cOEDf6cjCwkJNTe3cuXOC59PnI02aNKnBtyNBVHF5enqmpqZGRET8+OOPhJDGt1xeXr53715CiIuLS2JiIp/Pj4uLEzGhpmQWceCFoqivX7/S/3fo0GH+/PkVFRUPHjywtbUVPEHwl6aoqMjlcgU3h+VyufW1L7zca1BUVAwNDSWErF27lr6ETNinT5+OHDki1qJvfCqJiZjh8+bNq6//9EYSbHr1bSCDBg0ihMTExAiGC27RK2KUWOrcAAGaFc70AwAAZowZM8bU1DQyMrKsrGzq1Klfvnw5c+bMyZMnCSFfvnwhhAi6Ni4uLiZCh63or86CbqbZbPbnz595PJ6qqipFUf7+/hs3bhR98xwrK6tx48adPXt29OjRDg4O79+/T09PP3PmDH211ZEjR8zMzJKSktLS0nJyclJSUrS1tXV0dPLz85OSkrhcrpWVVX3Jg4ODY2Nj+/Tp061btw4dOmhoaBgZGWlqagYEBHh7e9MnIBFCdu/e7erqSlcv9BfooqKiTp060fFycnIIIfn5+ZJFpWeX6FvHCnN0dPTx8RkzZgz9ZZo+m6vOlmtHPXDggJeXF32vVXV19e+//17EhES0XCNzjYfXrl1zcHA4ePCgo6Nj7WY/fvyYlZVVVlb2zTffEEK8vLx27drl5eUlOEuNPtFO+NohY2PjU6dOBQQEODs7R0dHl5eXZ7AZI7sAACAASURBVGRkPHz4cOjQoTUmLbzcTU1NhRcNIcTGxmbPnj0rV660sbEJCgoyNTUlhBQVFV25ciU8PDwiIkL0oq8xLRGpxNoi6LGCJ+fk5NAd5bFYrNevXwuPopWUlPj6+iopKbHZbKlsevr6+nVuIJqamiNHjjx06BCHw3Fzc3v69GlCQkJeXl5kZOSUKVNEjKox20W8wTo3wKafQAggSov2eQEAAPKhkX1kFRQUzJ49u2vXrlpaWm5ubpmZmRRFxcXF0T9Uz549+9WrV9evXx8yZAghxN7ePiUl5datW0OHDqXHvn79mqKo5OTkGTNm2NnZeXp6Ll26VNDxl2g8Hm/RokU9evTQ1tZetGhRUVERPXzhwoVqamoWFhZ///33xYsXNTU1HR0duVxucnKyrq5u//79Y2Ji6ktOh+/atavwb/x6enp0f81nz561tbX18vLasGHDjh076D6yw8PD6Qv658yZQ/ecFh8fT99t1szM7Nq1a+JGjYuLoy8U4XA4ly5dauTy8vPzy87OFjyss+Vdu3bViFpWVmZqajp+/PjAwEBPT8+wsLAGJ9SYzHfv3q3xFuLi4rp3737u3LnaDZ49e5aeXVOnTr158yY90MXFpbi4mKIoLpe7a9euHj16EEK6dOmyc+fOkpISiqKKi4snTpzYoUMHCwuL+/fv//TTT3Pnzr148WLtSQsv99qLhvbq1StPT09zc3MdHR1TU9ORI0fu3buXPvImCFl70deeVn2pxNoi4uLi6NMLFy9enJube+zYMVVVVULIpk2bjhw5Qpe7bDbbxMTE3t7e3t7e0tKSvmhq9+7dUtz06ttAioqK5s2bp62t3atXr02bNnl6es6bN+/vv/+mq/Q6R8XGxgrPdhFvsKqqSsQGKAL69IOmYFGSnhgAAABQHycnJ/Lv83bkxNGjR9XV1SdMmEDfhDczMzM5OTk/P3/btm1MRwNo+yTbAOV2fwVSgTP9AACgbdLV1RWcklRDRETEuHHjpD7FpKSk1atXZ2ZmEkK0tLS0tLQMDQ05HE5kZKTUp9VILT8TWn6KALRWuAGCPEA1BQAAbVONLsVawJMnT7Kysvz9/cePHz9gwICvX78mJiZeu3YtMDCwhZMItPxMaPkpAtBa4QYI8gB9+gEAAEiHi4uLn59fSEgIh8Pp2rXrxIkTCwsLg4KC6Bv7AkCzwgYIjMB1UwAAIH1yfh1CSUmJiopK7fvtAkALEHcDlPP9FTQRzvQDAACQMrqTNABgBDZAaEk40w8AAAAAAEASqKYAAAAAAAAkgWoKAAAAAABAEqimAAAAAAAAJIFqCgAAAAAAQBKopgAAAAAAACSBagoAAAAAAEASqKYAAAAAAAAkgWoKAAAAAABAEqimAAAAAAAAJIFqCgAAAAAAQBKopgAAAAAAACShyHQAAABomzIyMqKjo5lO0UJKS0t5PJ6mpibTQaAt+/r1q5qaGtMp2qCMjAxdXV2mU4CsQjUFAADNIjExcfr06UynAABomKOjI9MRQFaxKIpiOgMAAEBrx+PxHj9+nJSU9ODBg6SkpOfPn1dXV2tpaXE4HBMTEw6HY2pq2qNHD6ZjtnHOzs6EEPk55imMz+fHxcUFBQVdvHhRR0fH3d19yZIlWlpaTOcCkHeopgAAAOpQWVn54sWLpP9z//79iooKdXV1IyMjzv8xNDRksVhMJ5Uj8lxNCbx8+fLAgQPh4eFcLnfy5Mmenp6jR49mOhSA/EI1BQAAQEit8unBgwfl5eUdO3Y0NjZG+dRKoJoSKCsri46O3rVr16NHjwYMGLBgwQJ3d3dVVVWmcwHIHVRTAAAgp6qqqp4/f16jfFJTUxs0aBDKp9YJ1VRtd+7c2bNnT0xMjIqKioeHh4+PDzpUAGhJqKYAAEBe1CifkpKSysrKapRPAwYMUFDA7UNaKVRT9cnLy9u/f39wcHBeXt706dNXrlw5ePBgpkMByAVUUwAA0GbVKJ8ePnxYWlraoUOHwYMHo3ySRaimRKuoqDh58uSOHTtSU1OtrKx8fX0nTJiAg6sAzQrVFAAAtB3V1dXp6emC8unRo0clJSXKysrGxsZWVlZ0+WRgYMBms5lOCpJANdVICQkJ27dvv3jxopGRkZeXl5ub2zfffMN0KIC2CdUUAADIsDrLJyUlpX79+gmOPpmZmSkrKzOdFKQA1ZRYUlJSQkJCIiIi1NXVFy5c6OPj06VLF6ZDAbQ1qKYAAEDGZGVlJSUl3bp1KyEh4fHjxzweD+WTnEA1JYGMjIzdu3eHhYXx+XwvL6/ly5dramoyHQqg7UA1BQAArR1dPtFu3br1+fPnGuWTqalpu3btmI4JzQ7VlMS+fPmyb9++HTt2lJaWLlmyZMWKFbjzL4BUoJoCAIBWR7h8unPnTkFBgaKiYv/+/QXlk4mJCa4DkUOoppqIx+Pt379/+/btX758mT9//urVq7t37850KADZhmoKAACYJ1w+JSYm5ufn1yifOByOiooK0zGBYaimpKKkpCQ8PDwwMLC4uBg1FUAToZoCAAAGCJdPd+/ezcvLQ/kEDUI1JUXl5eVHjhzZvHlzUVGRu7u7r6+vjo4O06EAZA+qKQAAaAnC5dO9e/dyc3PZbLa+vr6gdho6dGj79u2ZjgmtGqopqSstLQ0NDaWPUy1atGjt2rXo9w9ALKimAACgWQiXT/fv38/JySGE9O7dW3Dfp++//15VVZXpmCBLUE01k9LS0vDw8K1bt1ZUVKxZs8bb2xvXJQI0EqopAACQDuHy6cGDB58+fSKEdO/eXXD0afjw4Z07d2Y6JsgwVFPNisfjBQcHb9myRV1d3c/Pb/78+bjPNUCDUE0BAICEPn/+nJSUlJCQQFdQ2dnZ5N/lk5WVFc4aAilCNdUCsrKyNm/efPDgQX19/e3bt48fP57pRACtGqopAABorKKioidPnggOQKWlpZF/l0/Dhg3T0NBgOia0WaimWkx6evqGDRtiYmJGjx7922+/DRkyhOlEAK2UItMBAACg9SouLk5NTa2zfHJycuJwOJaWlpqamkzHBAApMzAwiI6Ovn379qpVqzgcjoODQ2BgoJ6eHtO5AFodVFMAAPD/1Sifnj17RlGUcPlkYWGhpaXFdEwAaAnDhg1LSEg4ceLEunXrBg4cuGrVKl9fX9y6AEAYzvQDAJBrX758SUlJqbN8opmbm3ft2pXpmACE4Ew/5pSXl+/evdvf319DQ2Pnzp1Tp05lOhFAa4FqCgBAvnz9+jU5OVlQPqWnp/P5fOHyyczMTFtbm+mYAHVANcWs7OxsX1/fY8eO/fDDD7t37x44cCDTiQCYh2oKAKCNa7B8MjU17datG9MxARqGaqo1uHnzpre397Nnz5YvX+7n54ebboOcQzUFANDWcLncx48f1yifOnXqNHDgQMF9n3r37s10TACxoZpqJaqrq/fu3bt+/XoNDY2QkBA7OzumEwEwBtUUAIDMq6ioSE1NFdz36fnz59XV1cLlE4fDwTk50AagmmpVPn369D//8z9Hjx6dMGHC3r17dXV1mU4EwABUUwAAsqeysvLFixeCo0/379+vqKhQV1c3MjISlE+GhoYsFovppADShGqqFbp48eKSJUuKi4sDAwPd3d2x2wF5g2oKAEAG1CifHjx4UF5e3rFjR2NjY5RPID9QTbVOPB5v48aNu3bt+uGHH8LDw3FbKpArqKYAAFojlE8AtaGaas0eP348b968Fy9ebNiwYdWqVQoKCkwnAmgJuHsvAECrUFVV9fz58yQhZWVlampqgwYN4nA4np6eHA5nwIAB+IICAK3TkCFDEhMTN2/evH79+itXruzfvx+93YA8wLEpAABm1CifHj58WFpa2qFDh8GDBwuOPqF8AhCGY1MyISUlZd68eenp6Vu3bl26dCnTcQCaF45NAQC0kOrq6vT09Brlk6qq6pAhQ3D0CQDajEGDBiUmJm7ZsmXFihXXr18PCwvT1NRkOhRAc8GxKQCA5lKjfHr06FFJSYmSklK/fv0E930aMmQIm81mOimAbMCxKdly+/ZtV1dXHo934MCB8ePHMx0HoFng2BQAgDS9efNGcN+nx48f83g8Qfnk5OTE4XDMzMyUlZWZjgkA0OyGDRuWlJS0ePHiiRMn+vj4BAYGYu8HbQ+qKQCAJsnKyhIcfbp169bnz59rlE+mpqbt2rVjOiYAAAM6deoUGRk5fvz4RYsW3bp1KyoqCl1TQBuDM/0AAMQjXD7dvn27sLBQUVGxf//+gq4jTExMvvnmG6ZjArQF169fT0xMFDw8fvw4IWT27NmCIRYWFiNHjmQgGYjp5cuXzs7Ob968CQ0NnTFjBtNxAKQG1RQAQAOEy6c7d+4UFBSgfAJoGdeuXbO1tVVSUqrdOwufz6+srLx69eqYMWMYyQbiKisrW758+d69e5csWfK///u/OGgPbQOqKQCAmoTLp8TExPz8/BrlE4fDUVFRYTomQNtXXV2tra1dUFBQ59jOnTvn5uYqKuKyBVly9uzZ+fPn6+npnTlzRk9Pj+k4AE2FagoA4F/l0927d/Py8thstr6+vqB2Gjp0aPv27ZmOCSCPli5dum/fvoqKihrDlZWVFy5cGBQUxEgqaIpXr15NnTr106dPJ0+eHDVqFNNxAJoE1RQAyCPh8unevXu5ubkonwBap8TEREtLyzpH3blzx8LCooXzgFRwudyffvrp7Nmzv/76q6+vL9NxACSHagoA5IJw+XT//v2cnBxCSPfu3QX3fbK0tFRVVWU6JgDU4dtvv/3w4UONgbq6uh8+fGCxWIxEgqajKGrbtm1+fn6zZs0KCwvD1acgo1BNAUCrEx8f37t372+//bYpjQiXT0lJSdnZ2USofOJwOFZWVl26dJFSZABoRuvWrfvtt98qKysFQ5SVlVetWvXrr78ymAqk4sqVKzNnzjQwMDh37lzXrl2ZjgMgNlRTANCKZGRkLFu27NSpUxEREa6urmK9tqio6MmTJ4LyKS0tjfy7fBo2bJiGhkbzBAeAZvTs2TNDQ8MaA1NTU42MjBjJA9L16tWrCRMmVFRUXLhwYeDAgUzHARAPqikAaBXKy8t37tz5yy+/8Pn86urqJUuWNHhxeYPlk6WlpaamZovEB4DmZWho+OzZM8FDAwMD4Ycg6woLC6dNm/bw4cMTJ06MHz+e6TgAYkCnogDAvNjY2EWLFr1586a6upoecufOndpPKy4uTk1NFZRPz549oyiKLp+cnJw4HI6FhYWWllbLZgeAluDm5rZhwwb6ZD8lJaU5c+YwnQikqUuXLleuXHF3d58yZcqePXs8PDyYTgTQWDg2BQBMysjI8PX1jYyMVFBQ4PP5guHt2rXjcrklJSUpKSl1lk80c3NznGcPIA8+fPigp6dHf2lhsVhv3rzBrYraHoqifvnll82bN2/evNnPz4/pOACNgmoKAJhRWlq6ffv2gIAAiqKELy4X0NXVzcjIIIT07NmTrp1MTEw4HA6OPgHIJzMzs6SkJIqiTE1N7969y3QcaC6HDx/28PBYsGDB7t27FRQUmI4D0ACc6QcADLhw4cLixYuzs7MFp/bVwGazTUxMQkNDORyOtrZ2C8cDgFbIzc3t0aNHhBBxu6gB2TJ37tzOnTvPmDEjKysrMjISPadDK4eKHwBa1MuXL+3s7CZNmpSVlVVfKUUIUVBQ0NXVtbe3RykFALTp06dTFEVRlJOTE9NZoHlNnjz54sWLsbGxkydPLikpYToOgCg4008MuEUggKxzdHSMiYlhOgW0KCcnp1OnTjGdAgDqIHqf/PDhw7FjxxobG1+4cAF3V4dWC2f6iefnn3+2tLRkOgWATKqsrMzNzc3Jyfn06VNOTk52dnZWVlZBQQHd+QSbzVZQUKiqqhL8xKOoqBgREcFms6UV4Pfff5dWUyBbLCwsli1bxnQKkILr16+zWKwRI0YwHQSkoMF98tChQ2/cuPHjjz/a2dldunRJTU2tZYIBiAXVlHgsLS2dnZ2ZTgHQdlRXV3/48OHVq1evX79+/fr1ixcv0tPT379/X15eXlVVZWhoOHjwYGlNC0el5Jauri523W2DnZ0dIaRjx45MBwEpaMw+ecCAAfHx8T/++OO4ceMuX76MggpaIVRTAMAkNpv93Xfffffdd2PGjBEenpWV9erVK9x7FwCEoY6SQwYGBnFxcfQRqsuXL2MdgNYGvVAAQGuko6MzYsSIHj16MB0EAAAYZmBgEBsb++bNm/Hjx6NTCmhtUE0BAAAAQKs2YMCA2NjYZ8+eOTo61nmLQgCmoJoCAAAAgNbO0NDw8uXLCQkJM2bMEHGDDYAWhmoKAAAAAGSAqanp+fPnL1265OXlxXQWgP9CNQUAAAAAsuGHH36Iiorav3//unXrmM4CQAiqKQAAAACQIZMmTdq/f39AQMCePXuYzgKAHtIBAAAAQKbMmTMnIyPDx8enZ8+eEydOZDoOyDVUUwAAAAAgY9atW/fhw4fZs2ffvHlTivd5BxAXzvQDAAAAANkTHBxsZmZmb2+fkZHBdBaQX6imAAAAAED2KCkpRUdHq6mpTZ48mcfjMR0H5BSqKQAAAACQSV26dLl48eKHDx/mzp1LURTTcUAeoZoCAAAAAFnVp0+fs2fPnj9/PjAwkOksII9QTUGTfP36tflaaHrjAAAgQ/CZApIZPnz4tm3b1q5d+9dffzGdBeQOqimQUEhIiLW1tYWFhcQthIaGjhw5csCAAQ2OiouLs7CwePfuncTTahlNz/ngwQMHB4dVq1Z5enoeOXJErNcWFxevX79+xIgRxsbGEydOnDp16tq1a9euXRscHCxxHgBoVaqqqm7evLlu3borV67QQ2Rl99igNvmZgt1yS1q+fLmrq+vs2bPfvHnDdBaQL6im2prs7OyWmdCCBQuKi4v5fL7ELbi7u/P5/Orq6gZHff78+ePHj63/AtMm5kxOTraxsVm5cuVvv/0WFBS0devWffv2NfK1Fy5cMDAwuH79ekRERGpq6oULFw4ePJiVlRUQEFBSUiJZHsk03xrYYus2ACMas4bfv3//0KFDW7duFfRgJiu7xwa1vc8U7JZb3t69e/X09KZNm9bCcxjkHKqpNqWoqGjWrFktMy1FRcUePXo0pQU2m62rq9uYUQ4ODpmZmQMHDmzK5FpAE3OuWLHC3Nzc0tKSEKKiorJ06dJVq1Y15tSUmzdvOjg46OnpxcXF6enp0QM7d+58+PDhWbNmteTXrOZbA1ty3QZoeY1cwy0tLb29vYWHyMrusUFt7DMFu2VGqKioREdHf/jwwcvLi+ksIEdQTbUdPB7P2dm5DZzvIZ+ys7NjY2NHjhwpGGJtbc3lco8dO9bga318fCorK/39/ZWUlGqM2rx5c4v9RNd8ayDWbWjbxFrDlZWVmzkOSAF2y0zp06fPkSNHDh8+fPz4caazgLxANSV9ly5dWrx48dKlSy0tLcPDwwXDT5065e3tvXLlynHjxq1fv768vJwQ8vjx41WrVvXu3ZvH47m7u2tqapqZmQmf8ltnazk5OR4eHv7+/h4eHlOnTi0oKCCEnD17Nj09PT8/38PDY8eOHaJDUhS1b9++RYsWmZub29ravnz5khDy9OnTtWvX6uvrZ2RkbNq0qVevXgMHDoyPjy8rK1u2bFmfPn169eolOFlfIDY21tbWtkuXLmPHjhUkr7N92vnz5z09PX19fX18fGqcJFDfqLy8vODg4Lt37zZmjj148MDDw2PWrFlmZmahoaFVVVX08OTk5Llz5wYGBi5btmzx4sWi5099M5k+GY/FYo0aNerTp0+///77N998ExAQUFlZWSOnuFNMS0sjhPTt21cwpF+/foSQ27dvE0ISEhJ69ux5+fLl2i9MTU19/Phxp06dRo8eXXts3759Bb9kt/waWOdqIHqKTWkZoIlkYu9dQ+N3j/VtNXVGyszM3LZtm5GRUWFh4dixY7/99lt6eH3k8DMFu+VWu1ueOHGij4/PwoULnz9/zlQGkC8UNBohJCoqSvRzIiIiZs6cWV1dTVHUli1bCCGxsbEURe3cudPKyqqiooKiqPz8/H79+o0YMYLP52dnZ9N72yVLljx9+vTRo0ft2rWbMWOG6NZsbGymT59OP2fw4MEuLi70/xMmTNDT02vMewkICDh8+DBFUVVVVYaGht26dePxeLm5ua6uroQQT0/PpKSkL1++mJub9+7d28vLKy0t7evXr8OGDevdu7egETs7Ow0NjZ9++umvv/7as2dP+/btdXR0uFxufe1TFHX8+HELC4vS0lJ6PmhpaXXr1o1urb5RCQkJ1tbWhJBTp05RFCV6jr17905VVfXt27cURbm5uRFCOBzOzz//TFGUvr5+QkICRVElJSXW1tYNzqL6ZnJBQUH37t2NjY0pivL19T127Bg9vEZOcadIX5T8559/Cg9s167dyJEjKYq6ePGiiorK8ePHa79w//799NsU3T4ja2Cdq4HoKTalZdFzgKIoR0dHR0fHBp8GbUwjl7us7L2fPHlCCNm/fz8l5u6xvq2mzkiXL182MDBgs9kbN24MCwszMzPLzMwUkUoOP1OwW276brn59sllZWVDhw7lcDjl5eXN0T6AMFRTYmiwmsrNzVVXV3/z5o3g4bRp09LS0nJyclRVVY8ePSp45qFDhwghERERFEWtWbOGEJKfn0+PGj58eL9+/US0RlGUjY3N1q1b6eGzZ88eNGgQ/X8jP48zMzO1tbXpfTFFURs2bCCEnDx5kqKokJAQQkhKSgo9auPGjYSQR48e0Q/9/PwIIbm5ufRDOzs7HR0dQbPr168nhAQFBdXXPo/H6969e2RkpOAl06ZNoz/eRIyiKOrq1avCVUp9c4yiqJUrV/bs2ZP+/8WLF4SQ0NBQiqIqKioIIf/5z3/oUcITqk99M5l+OSFk06ZN06ZNE36JcE5xp0i/qX/++Ud4oIaGhoGBAf1/VVVVnS/87bffCCG2trYiGmdkDRSxmolYgk1sWTRUU/KpMctdVvbe1L+rKarRu0cRW019kebPn08IefXqVYOR5PAzhYbdchN3y826T3758mXHjh2XL1/eTO0DCChKfFALaktISODz+d999x39UEtL6/Tp04SQP/74g8fj9ezZU/DMCRMmEEL++ecfV1dXNptNCFFU/O+y0NXVffXqlYjWCCHx8fGEkNLS0uPHj9+7d48S8+bft2/frqysXLBggWCIu7u7iooKIYQOo6CgIAhDCBGc9t2rVy9CCP0jHz2kY8eOgkbmzZv366+/JiUl6ejo1Nn+zZs3s7OzjY2NBcMFFwCIGEUIad++vXD++uYYISQzM1NwPnq/fv00NTU/fvxIvwVbW9ulS5empaVt3bp15syZDc4lETN55syZ4eHhmzZtSklJEX6JcE5xp0ivHjVOpi8pKaHnueBd10Y/4e3btyIaT0xMbPk1sMHVrM4l2MSWASQjK3vv2hq5exSx1dQXSUlJSVFRsU+fPg1mkMPPFOEX1obdMmkFu+W+ffsGBwfPmTNn1KhR9vb2DCaBNg/VlDQ9efKksrKSoigWiyU8/P3794SQwsJCwRBNTc327dtnZWVJ0BohpLq6OjAw8NGjR15eXubm5omJiWLlfPbsmaqqqvBVAfWpMWn6YX092Orp6SkrK5eWltbXflBQEBH6HBWWnp5e3yix2NvbnzhxIjY2dtSoUUVFRVwu187Ojh515swZDw+PvXv3nj59OiYmZsSIEaKbEj2T586dGx8ff+DAgV27dtXXglhTpL+yFBcXC4ZUVFSUlpbq6+uLzkl3S/X27duqqirBB2ENjKyBjV/NWqZlABFkZe8tMRFbTdMjyednigjYLbcSrq6uV69enT9//pMnTzQ0NJiOA20WeqGQpo4dO5aVldHdCQiUl5fTPyPVvp2c6C/K9bXG5/Pt7e1TU1Ojo6MbLAnq1L59+4yMDMHtSmh5eXkSNCVMQUFBUVHRyMiovvbpnwbpj5AaRIwSi4uLS1hYmJubm5+f34oVK06cOGFlZUWPUlJSioyMPHr0KCHE1taW/qytj+iZzOPxIiMjZ8+eHRwcnJycXF8jYk3R2NiYzWYL949E/65pYGAg+i3r6+v379+fvqdnfc9hZA2UYDVrvpYBRJOVvbfE6ttqpBJJPj9TRMBuuTEtt4zg4GBlZWUPDw+mg0BbhmpKmkxMTAghfn5+gl/akpKSTpw4YWFhoaamdu7cOcEz6ZMHJk2aJEFr9+7du3r16qhRo+iB9M9U9P8KCgpcLrfBnIIeFARDXr9+vWfPnsa/0zq9e/eusrLS2dm5vvYHDRpECImJiREMF9xOUcQosZSVlb148SI5Odnf3//AgQNTpkyhh5eXl+/du5cQ4uLikpiYyOfz4+LiRLQjYiYTQuiP1Z07d6qpqS1evLjO0x7EnWL37t2nT59+/fp1wZDr168rKys7ODjQD+v7+VZRUTE0NJQQsnbtWvpiLWGfPn06cuQII2ugBKtZ87UMIJqs7L0lVt9WI3pf18TGm5i5NX+mCNqs81XYLTem5Zahrq5+8ODBc+fOnTx5kuks0GbhTD9psrKyGjdu3NmzZ0ePHu3g4PD+/fv09PQzZ84oKioGBAR4e3vTZwsQQnbv3u3q6vrjjz+S/zu5S9Dpak5ODt0HUX2tJSUlEUKOHDliZmaWlJREXyedkpKira2to6OTn5+flJTE5XJNTU1rnBouMGbMGFNT08jIyLKysqlTp3758uXMmTP0jubLly/CYehsgp+X6DvJ0l24EkLYbPbnz595PJ6qqipFUf7+/hs3bjQwMNDX16+zfU1NzZEjRx46dIjD4bi5uT19+jQhISEvLy8yMnLKlCkiRuXk5BBC8vPzhVPVnmMsFis4ODg2NrZPnz7dunXr0KGDhoaGkZERfbLHgQMHvLy86Hs4qqurf//99yIWJX0SRZ0z+d27dx8/fhwzZgwhYfvrxwAAIABJREFUxN/f39vbOzQ0dOHChXQS4ZxiTZEQsmbNGktLy8ePHw8ZMqSioiI4OHj9+vXa2tqEkGvXrjk4OBw8eNDR0bH2C21sbPbs2bNy5UobG5ugoCBTU1NCSFFR0ZUrV8LDwyMiIjQ1NVt+DbSysqpvNROxBJvYMoBkZGXvLZio4Pavjdw91rfbf/36dX2RuFxudXV1UVFRp06dRM89+fxMwW5ZVnbLo0aNWrhw4aJFi4YPH17f/Z0BmqSZerdok0gjekjn8XiLFi3q0aOHtrb2okWLioqKBKPOnj1ra2vr5eW1YcOGHTt28Pl8iqLi4uLow/2LFy/Ozc09duyYqqoqIWTTpk1VVVX1tbZw4UI1NTULC4u///774sWLmpqajo6OXC43OTlZV1e3f//+MTExonMWFBTMnj27a9euWlpabm5udNe3cXFx9O95s2fPfvXq1fXr14cMGUIIsbe3T0lJuXXr1tChQ+mxr1+/pigqOTl5xowZdnZ2np6eS5cuFfSPVF/7FEUVFRXNmzdPW1u7V69emzZt8vT0nDdv3t9//01/Ztc5KjY21sbGhhBiZmZ27do10XMsLi6ua9eugiueCSF6enpv374tKyszNTUdP358YGCgp6dnWFhYg4u7zpn8xx9/6OrqLl++nF589K11lZWVg4OD4+PjhXNKMEWKou7fvz99+vQ1a9bMnDkzODiYngq9aLp3737u3DkRr3316pWnp6e5ubmOjo6pqenIkSP37t1L/45Ia/k1sL7VTMQUm9Jyg9Cnn3xq5HKXib333bt36et2OBzOpUuXaux2REeqb6upM9KuXbvoniHmzJkj6IJPBHn7TKGwW5bGbrnF9sk8Hq9fv35jxowRfKoCSBGLanKHQvKDxWJFRUU5OzszHQTqdfToUXV19QkTJhQUFOTl5WVmZiYnJ+fn52/bto3paMA8Jycn8u+Tf0AeYLmDxPCZ0qxacttMSEgYOXLkgQMH5s6d2wKTA7mCM/3aLF1dXcHpEzVERESMGzeuhfO0gKSkpNWrV2dmZhJCtLS0tLS0DA0NORwOfXuo2lp+FsnhQgEAcbXOHUXrTNWsxP1MgdZs+PDhS5YsWbFihb29fdeuXZmOA20Kqqk2q0bXOvLgyZMnWVlZ/v7+48ePHzBgwNevXxMTE69duxYYGFjn81t+FsnhQgEAcbXOHUXrTNWsxP1MgVZuy5Yt586dW758OX2WPoC0oE8/aDtcXFz8/PxCQkI4HE7Xrl0nTpxYWFgYFBSE+7oCAIC48JnSxqipqe3bt+/48eN//vkn01mgTcGxKWg72Gz2L7/88ssvv5SUlKioqNS+uSEAAEAj4TOl7bG3t3d0dPTy8rKxsenQoQPTcaCNwLEpaIPat2+Pjz0AAJAKfKa0Jbt37y4uLt68eTPTQaDtQDUFAAAAAHKhe/fuAQEBu3btevr0KdNZoI1ANQUAAAAA8sLT03Po0KE+Pj5MB4E2AtUUAAAAAMgLBQWF4ODgf/75B7ehA6lANQUAAAAAcsTU1NTV1XX58uU8Ho/pLCDzUE0BAAAAgHzZvn07l8vdtm0b00FA5qGaAgAAAAD5oq2tvWHDhh07drx584bpLCDbUE0BAAAAgNzx8vLS09Nbs2YN00FAtqGaAgAAAAC5o6SktG3btpiYmFu3bjGdBWQYqikAAAAAkEeTJ0/+4YcfVq5cSVEU01lAVqGaAgAAAAA5tW3btrt37547d47pICCrUE0BAAAAgJwyNTWdPn36qlWrKioqmM4CMgnVFAAAAADIr61bt2ZkZISHhzMdBGQSC+eJNh6LxWI6AgA0iaOjY0xMDNMpoEU5OTmdOnWK6RQAUIfWs0/++eefo6OjX79+raKiwnQWkDGopsQQHR3NdAQ5Ul5efvLkycuXL5uYmCxfvlxBAcdRQQp69uxpaWnJdApoUXfu3Pn48SPTKUA6fv/9d0LIsmXLmA4C0tF69sl5eXm9e/feuHHjypUrmc4CMgbVFLRGiYmJc+fO/fTpU2BgoKenJ9NxAACgVXB2dib4cROax+rVqw8cOPDmzRs1NTWms4Aswe/90LqUlZWtXr16+PDh3377bWpqKkopAAAAaAG+vr5VVVVBQUFMBwEZg2oKWpGUlBRLS8u9e/fu2bPnr7/+6tmzJ9OJAAAAQC507tx56dKlO3bsKCwsZDoLyBJUU9AqVFVVbd++3dTUtEOHDklJSZ6enujzAwAAAFrSsmXL2Gz2rl27mA4CsgTVFDDvyZMn5ubmmzdv/uWXX65fv963b1+mEwEAAIDcUVdXX7Zs2X/+85/i4mKms4DMQDUFTKIPSZmYmLRr1+7x48e+vr7ouw8AAACY4u3tTQgJCQlhOgjIDHxzBcY8e/Zs2LBhmzZt2rx5882bN/v37890IgAAAJBr6urqixcv3rlzJ5fLZToLyAZUU8AAiqLCwsJMTExYLNbDhw99fX3ZbDbToQAAAADI8uXLy8vLw8PDmQ4CsgHVFLS0t2/f/vDDD15eXt7e3gkJCQMGDGA6EQAAAMB/aWhoeHh4BAYGlpWVMZ0FZACqKWg59CGpQYMGFRYW3r17d9u2bUpKSkyHAgAAAPiXlStXfv78+ciRI0wHARmAagpayPv370ePHr1kyZIlS5Y8ePDg+++/ZzoRAAAAQB10dHTc3Nx27tzJ5/OZzgKtHaopaAkRERHGxsafPn26c+fOtm3blJWVmU4EAAAAUK8VK1a8evXqjz/+YDoItHaopqB5ffr0adKkSfPmzZs3b97Dhw9NTEyYTgQAAADQAH19/fHjx//v//4v00GgtUM1Bc0oJiZm4MCBT58+jY+PDwoKateuHdOJAAAAABplxYoVCQkJiYmJTAeBVg3VFDSLnJycadOmTZ8+3dHRMSUlZcSIEUwnAgAAABDDyJEjzc3Nd+7cyXQQaNVQTYH0xcTEGBkZPXr0KDY2NjQ0VFVVlelEAAAAAGJbvnz5mTNn3r59y3QQaL1QTYE0ff782c3Nbfr06dOmTUtJSfnhhx+YTgQAAAAgoWnTpvXo0SMkJITpINB6oZoCqbl06ZKR0f9j7z7jmsj6t4GfQEBBxQaoCNjFAjaUIk1XxYqIFBtiBcuq6F9dsRd27bu3uDYsq6Kggh1dOxZi71LUtWABFUREirSQeV7M/eRmgYwhJDlJuL4v/JjCmWvKmZlfMnNiefHixZMnT4aGhtaqVYt2IgAAAADZ8fn8yZMn79q1Kzc3l3YWUFGopkAOvn37NmnSpIEDBzo4OCQkJAwaNIh2IgAAAAA58Pf3z8/Pj4iIoB0EVBSqKaisc+fOWVpanjhx4vjx45GRkXXr1qWdCAAAAEA+DA0Nhw0btnHjRoZhaGcBVYRqCmSXlZU1adKk/v3729vbJyQkuLu7004EAAAAIGczZsyIj4+PjY2lHQRUEaopkJFAIOjSpcvx48ejoqIiIyPr169POxEAAACA/HXp0sXW1hZjUUC5UE1BheXl5QUFBbm4uLRp0+bRo0eenp60EwEAAAAo0NSpU48dO5aamko7CKgcVFNQMTdv3uzUqVNoaOjWrVtPnTrVqFEj2okAAAAAFMvb27tmzZp79uyhHQRUDqopkFZ+fn5QUJCTk1Pz5s3j4+MDAgJoJwIAAABQBj09PV9f3+3bt2MsCigF1RRI5fbt2507d966deuWLVvOnDnTuHFj2okAAAAAlMff3//169eXL1+mHQRUC6op+IGioqI1a9Y4OTmZmZnhKykAAAComqysrOzt7Xfs2EE7CKgWVFPAJS4uztbWdsWKFcHBwWfPnjUzM6OdCAAAAIAOf3//o0ePfv78mXYQUCGopqB8QqFwzZo1Xbt21dfXf/To0bx587S0sLUAAABA1TVs2LDq1asfOHCAdhBQITg/hnIkJCTY29svX758xYoV165da9WqFe1EAAAAAJTp6+sPHTp0//79tIOACkE1Bf9SXFy8Zs0aa2trPp//8OFDfCUFAAAAIObr63v37t1nz57RDgKqAifK8D+vXr3q2bPnsmXLli9fLhAILCwsaCcCAAAAUCE9e/Y0NTUNDw+nHQRUBaopIIQQhmG2b9/esWPHb9++3bx5c968edra2rRDAQAAAKgWLS2tESNG7Nu3Dz88BSw+7QBA35s3b8aNGycQCGbPnr1ixQpdXV3aiQAAAAghJD09PSsrS/wwNzeXEPL69WvxMwYGBoaGhhSSQRU2ZsyYdevWCQQCJycn2lmAPh4K66qMYZgdO3bMnj27adOme/fu7dKlC+1EAAAA/7Nr166JEydyvGHnzp0TJkxQWh4AVseOHe3s7EJDQ2kHAfpwpZ/mi4mJKff5d+/eubq6/vzzzz///PO9e/dQSgEAgKoZOnSojo6OpFd1dHSGDh2qzDwALF9f36ioqIKCAtpBgD5UUxru0KFDffr0EQgEpZ6Piorq1KlTSkrKjRs3Vq9eXa1aNSrxAAAAONStW7dfv358fjk3JvD5/P79+9etW1f5qQBGjRqVlZV1+vRp2kGAPlRTmuzly5fjx49nGMbX15e91pwQ8unTJ3d392HDhnl7e9+7d69bt250QwIAAHDw9fUtLi4u+3xxcbGvr6/y8wAQQkxMTHr27IkfngKCakqDFRQUeHp6FhUVMQyTkpLyyy+/EEKioqIsLS3j4uIuX74cGhqqr69POyYAAACXwYMH6+nplX2+evXqAwcOVH4eANaoUaNOnz795csX2kGAMlRTGmv27NkJCQlFRUWEEKFQuHXr1l69eg0bNmzkyJHx8fEuLi60AwIAAPxY9erVPTw8St09paOj4+npic8EgSIvLy8dHZ2oqCjaQYAyVFOa6dSpU1u2bCl5aQSPx7t///6JEyc2btyIww8AAKiRkSNHsh8OihUVFY0cOZJWHgBCSM2aNQcPHoyf8QVUUxro3bt3o0aNKvWkSCT6/v378ePHqUQCAACQmaura6nRJurUqdO7d29aeQBYPj4+N27c+PjxI+0gQBOqKU1TVFTk6emZl5dX9pfEioqKdu/e/ffff1MJBgAAIBs+nz98+HDxj8vr6OiMHDmSY+R0AOXo27evnp7eyZMnaQcBmlBNaZqgoKBHjx6VuiJCjMfjjRs3LjMzU8mpAAAAKmPEiBGFhYXs/4uKikaMGEE3DwAhRE9Pr2/fvseOHaMdBGhCNaVRTp069Z///EcoFJb7qq6urkgk+vr16969e5UcDAAAoDIcHR1NTEzY/zds2NDBwYFuHgCWh4dHTEzM169faQcBalBNaY7k5OTRo0fzeLyST+rq6mppaRFCzM3Nx44dGxkZmZ6eHhgYSCkjAACALHg8nq+vr66uro6Ojp+fX6mDHQAtgwYN4vF4uI2iKuOVvbsG1JFQKHR0dLx7965IJNLW1mYYRiQSmZqaDhw4sFevXj179jQ0NKSdEQAAQHaPHj3q3Lkz+5+OHTvSjgPwX66urgYGBocPH6YdBOj4VzV18+bNP/74g2IakFl8fPyzZ88IIbq6ug0aNDA2NjY2Nq5RowbtXPInlx92+OOPP27evFn5dgA0yf/93//Z29tXshFvb2+5hAEo19mzZwkh/fr1ox0ENFlFzzS2bt06Z86cz58/4xdoqqZ/Xen3/v17FNbq6MuXL9++fevYsWOfPn0GDx5sa2vbrFkzzSulkpOT5bV93rx589atW3JpCkAzHD58+P3793JpJzk5ufLtAJSrSZMmTZo0oZ0CNJZsZxoeHh75+fkXLlxQRCRQffyyT+FHnUE1RUZGDhs2TF6t2dnZYVMHEJPjXSizZs3y8fGRV2sAJb169YoQ0qJFC9pBQDPJdqbRsGFDW1vbY8eOubu7KyIVqDiMQgEAAADqoUWLFiilQAV5eHhER0dLGlQZNBuqKQAAAAAA2Xl4eGRkZFy7do12EKAA1RQAAAAAgOxatmxpaWmJn/GtmlBNAQAAAABUypAhQ06dOkU7BVCAagoAAAAAoFL69u375s2b58+f0w4CyoZqCgAAAACgUuzt7evVq8f+JBpUKaimAAAAAAAqRVtbu2fPnufOnaMdBJQN1RQAAAAAQGX17dv3ypUreXl5tIOAUqGaAgAAAACorP79++fl5cXGxtIOAkqFagoAAAAAoLJMTU3btWuHi/2qGlRTAAAAAABy0K9fP1RTVQ2qKQAAAAAAOejbt29CQsK7d+9oBwHlQTUFAAAAACAHzs7O+vr658+fpx0ElEf2aurLly979+7ds2fPq1evnj59euPGDTnGUhHZ2dmKa6HyjQMAaIy0tLSIiIiQkJCEhATaWaBScHSDqqx69eouLi642K9KkbGaSkpK8vT07NOnT2JiYsuWLdu1axcdHS3fZHRt3rzZycnJzs5O5hZCQ0NdXFzatm37w5diYmLs7OzevHkj87RkcOTIEVtbWx6PV61atT59+vTv379fv36Ojo6GhoY8Hu/p06exsbELFy6s4rsDKqtG+dR9NoVCYcnNtdRDbhcvXpwwYQKPx+PxeP369YuIiFB02sOHD9vY2LBTnDVr1qNHjxQ9RbXw/PnziRMnOjk5Xb58uXPnzllZWcqZboW2FuUEUOv+yHHg0zyTJ0/m8XiGhoYdOnSwsLDg8Xj169e3trZu3ry5lpaWnp6ekvNQOax/+fLll19+cXd3d3d3d3Z27t+//+HDh6X5Q83eE/bt2/fixYtCoZB2EFAWpoRDhw6VekaSsWPHjh07lmEYkUgUGRlJCAkKCvrhX3348IHjoUopKiqysrJq06aNzC0IhUJHR8eGDRv+8KXDhw+bmJjEx8fLPC3ZHD9+nBDi4OBQ8smCggIXF5ddu3aNGzeOELJz584ftqO01Sr99vlDXl5eXl5eP3ybElYNrV5Qcrq0tkB5uXHjRsnNtdRDaRgaGhJC3r17p7iQJRf4iRMnCCFWVlaKm5wMCCGHDh1SWjultvyhQ4fOnz+fYZjMzMzw8PDKx5CSDFuLogOodX/kOPBpnjFjxixbtqy4uJhhmIsXLxJCfH192ZeePHliYGAgEomUHEleh3UpnT9/vlGjRlOnTs3Pz2efuXr1auPGjYcMGfL9+/cf/rlq7gnlcqbx7NkzQsj169flEglUn4zfTf39998tWrQghPB4vAEDBkjzJ5mZmSNHjpT0UNXw+fzGjRtXpgVtbW1TU1NpXvL09ExJSWnfvn1lJieD1q1bE0J0dHRKPqmrqztp0qTu3btPnz5dmkbUa7VWlKJXDa3FVWq6tLZAebG3ty+5uZZ6KI3atWsTQurWrSvnZP9fqQXOdr169eopaHKqr+yWf+7cOXYt1K5dW5mdQoatRdEB1Lo/chz4NI+Wltb8+fO1tMo5j7KyshoxYkRBQYGSI8nlsC6ld+/eeXh4NGvWbPPmzdWqVWOfdHZ2joyMPH78+Pz586VMq5F7QgsLCxMTk2vXrtEOAkoiSzVVWFj4+fPnWrVqSf8nubm5Pj4+4ksXSj0EKso9BhBCRowY0aZNG11d3R+2gNVaGbQWl0auplKbqzRbb0k8Hk/8r9yVXeAKnZzqK7tA8vLycnNzaeWp6NaieQFANnPnzuVYd3PnzuXz+crMQ+RxWJdeYGBgbm7u4sWLSz3fvXt3BweHzZs3JyYmcreg2XtCZ2fnq1ev0k4BSlLhaurq1at+fn4Mw0RERPj7+/v7+//8888l35Camurv7x8cHOzv7+/h4fHlyxdCyLFjx549e5aenu7v779+/fpSDwkhDMNs27ZtypQptra2rq6uL168IIQ8evRo7ty5zZs3z83NnThxoqGhoY2NzevXr38YstzWEhISFixYYGFhkZycvGzZMnNz8/bt21++fDk/P3/WrFktWrQwNzcvez3xpUuXXF1d69Wr17dvX/Gky22fdeLEiYCAgHnz5s2YMePjx48lm5L00ufPnzdt2nT79m1pZvnevXv+/v4jR460sbEJDQ0VX5X7+PHjsWPHrl27dtasWVOnTmWfFAgEZmZmZ86c+eESE1u+fHm5z6vCalU+KVdNYmLiwoUL27Vrl5KS4u7uXq9ePRsbm1u3bhFCDh06VKtWLTMzM0LIt2/fgoODtbW17e3tSZml98Mwf//999SpUwMDA+3t7Xfs2CF+/vDhw9OnT58zZ07//v0XLVrEfhrKkbbsdKWcTY554UjC/Vflbrccyt0Of6hCHYH6ii53Nk+cOFGrVi0ej7dhw4bCwkJCyM2bNxs1arRy5cpye1lKSsrq1astLS0zMjL69u3bpEkTKZeVEpRaIHv37g0ICCCEREVF+fv7r1mz5octlLulcaydCqG+8KU/Ikg6EpXbTSqUqtw3V/TA98M9Rrn7NI6pSFJ2N8Ix6YqeCXDsPbhvD2vevPnOnTsrdPiTtD8sd4OXfoVKOqyX2/K5c+f4fL6uru6pU6fy8/MnTpzI4/EsLCyuXLlCCHn37p2dnZ2Xl9f3799PnjzJ4/F69uxZttkuXboIhcIDBw5UsldS74yV4eLiIhAIcOtUVVHysj8prxZlP1Ncv349+zAnJ4eUuG+qR48ew4YNY//fsWNH8WXEgwYNatq0qbiRUg9XrVq1Z88ehmGEQmG7du0aNmyYm5v78ePH3r17E0J+/vnnhISEhw8fVqtWbfjw4T9MWG5raWlpo0ePJoQEBATcv38/KyvL1ta2efPm06ZNS0xMzM7O7t69e/PmzcWN9OvXr379+uPHjz979uyWLVv09fVNTExycnIktc8wTHh4uJ2dXV5eHsMw6enpRkZG4svHJb0kEAicnJwIIYcPH2YYhnuW37x5U6NGjaSkJIZh/Pz8CCHW1tYzZ85kGMbCwkIgEDAM8/37dycnJ/b9p0+f1tPTk3QTAntdb48ePdiHQqEwLi5OfKtYfHw8KXGBtSqsViXfNyX9qrl27Vq7du20tbVnzpx5+fLlI0eO1K9fX19fn70zxNXV1dTUVNyslZWVnZ0d+/9Si4tDWFjYiBEj2Av0f/vtN0LIpUuXGIb5448/HBwcCgsLGYZJT09v1aqVs7OzSCTiXsglp1uhLZBjXiQl4f6rcrdbDpK2w1Kba6mH3B2BYZiWLVsSQtjerYQVXarrSTmbQUFBhJC7d++yzxcUFNja2jISetmZM2fatGmjra29dOnS7du329jYpKSkcC9bosT7pkotkPT0dELIr7/+Kk37krY07rXDQZp9nUIXfskAFeqPko5E5c5ChVKV+2YZDnwcnULSPk3SVDiUuxuRNOmKngn8cO/BKnXflKQZ4V6h5c6IpA2+3HVUocO6pJZHjBihq6vLLvbCwkIzM7PevXuL58vb2/vVq1f37t0jhJRcwiWFhIQQQry8vLh7pWruCeV1psF+NXfnzp3KNwWqTyHVFPshAcMwo0aN6tChA/t/jtPulJSUBg0asHtVhmGWLFlCCDl48CDDMOylt+np6exLjo6OrVq14o7H0drmzZsJIU+ePGFfWrp0KSHk4cOH7EP2C+u0tDT2Yb9+/UxMTMTNLlq0iBASEhIiqf3c3NxGjRpFRESI/2To0KHsQYXjJYZh2B8lYI+d3LM8Z84cMzMz9v///PMPISQ0NJRhGPYTmj///JN9qeSEhEKhpAXF7sgMDAzs7Ozs7Oy6du1qZGRUp04d9tWyZxh0VytDYxQK6VfNuHHj+Hw+e1gSR12yZAnDMEOGDCl5yGGXNvt/KauptLS02rVrv379Wvxw6NChiYmJqampNWrU2Ldvn/idu3fvJoSEhYVxpy01XelnU9K8cCeR9Fcc260kkrZD7mqK4ewIzL+rKe4lIJcVLc05RNnZfP/+PZ/PnzhxIvv8qVOngoODOXrZhAkTCCEvX77kmPGS1KKa4t7SONYOB2n2dQpd+KUCSNkfOaYuqZtUKFWpN8tw4GMkdwpJ+zSOmZJE0m6Eoz9W6EyA+dHeg1WqmpLh8FfujHBv8GVXqPSHdY6WY2JiCCHiAnLmzJm6uroZGRkMw+Tl5Q0dOpRhGHb4MbaMKevYsWOEkC5dujCcvVI194TyOtMQiUQNGjRYt25d5ZsC1Sf/i3ovX75MCMnLywsPD2eL8h/+yY0bN4qKiiZNmiR+ZuLEiezootra2oQQ8cXHpqamL1++rGRr4guL2ZtlxfdrmpubE0LYj9bYZwwMDMSNjBs37tdff71//76JiUm57cfGxn78+NHKykr8vPgaZY6XCCH6+vol83PMckpKyvfv39n/t2rVytDQ8P379+wsuLq6BgYGJiYmrly5csSIEaVa49ClSxd2lRFCioqKXF1dy30b9dVKhfSrRltbm8/ni7clDw8PXV3duLg4ucQQCAQikahZs2bsQyMjoyNHjhBCTp48mZuby17Nwho0aBAh5MqVK6NHj5Z+IUs/m5LcunWLI4mkv+LYbiWRYTtk/bAjlH2z8le0WLmzaWpq6u3tvX///lWrVhkaGkZGRi5dupSjl+no6PD5fHa4II3BvaXJZe1QX/hS9keOqUvqJhVKVerNkibHfXSTRNI+7fDhw5JmiiNnRXcjFT0TqNDegyXD4a/cGeHe4CWtUGkO6xwt79y5s1mzZvv27WPHg3ny5IlQKIyKigoICDhy5IinpychpGnTpoSQlJSUcmf/27dvhJAmTZqQyu0zqXfGyuDxeE5OTlevXp0zZ46SJw3KJ/9qqri4eO3atQ8fPpw2bZqtra00F8g+ffq0Ro0aJW8FqQzpWyt17yP7UCQSlfvmpk2b6urq5uXlSWqf/Wq71Fg6LPYDmHJfqpABAwYcOHDg0qVLvXr1yszMzMnJ6devH/vS0aNH/f39t27deuTIkaioKGdnZxna19HRmT17drkvUV+t6kVHR8fExEReF0zHx8cXFRUxDFNqi3379i0hJCMjQ/yMoaEhexGFXKYrPZmTVHS7lWE7VCj5rmgxSbM5a9asAwcObN++fc6cOenp6c2bNw8PD69SvaxCW5psa0ddFj7HDlYI7FWkAAAgAElEQVQR3USGAx8HSfs02Y4alTz8VehMQErympHK7+QlHdY5WubxeGPGjAkODv706dPLly9tbGy0tbX379/PVlPh4eHk/98zlpKSkpubW6NGjVKNs/VhyRq7ZB7pe6W6dEZJXFxcFi1aVFxcLENBDupFxhHSJRGJRAMGDIiLi4uMjJR+j6avr5+cnJycnFzyyc+fP8uWQb6tiWlpafH5fEtLS0ntsx/IsXuoUjheqhBfX9/t27f7+fktXrx49uzZBw4ccHBwYF/S0dGJiIjYt28fIcTV1ZWt32TAfjpViiqsVrVTWFhoYWEhl6YMDAzy8/NLjY9UUFDAfrJbdgAPeU1XejInqdB2K9t2qGhyXNGEkKdPn+bk5EiazW7durGDZZ06dcrNzY1UvV5W0S2tomuHYxtTtYUvaeoK6iYyHPg4SNqnybZI5XX4kyN5zYhcdvLlHta5Wx4zZoxIJDpw4MDmzZunT58+ZswYgUAQExPTqFEj9gufmjVrurm5MQxT7q/uXrhwQVtb293dvdw80vRKzdgTuri4fPv27cmTJ3RjgBLIUk1xXF1z586d8+fP9+rVi33Ifvj03ylpabF3WJV9yP5227x588Svvnr1asuWLTJkk3trYm/evCkqKvLx8ZHUfocOHQghUVFR4udFIlFxcTEhhOOlCsnPz//nn38eP34cHBy8a9euIUOGsM8XFBRs3bqVEOLr63vr1i2RSMRe90w4P2BjV40010qpwmpVL2lpaZ8+fWKviODz+Tk5OeLVnZOTI14ppZaeJF27diWELF68WPyH9+/fP3DggJ2dXa1atdifa2Sx14IOHjyYu0Epp1uWpHnhTiLprzi223JxbIc/xP1Js/R9oRTZVjTHhIKCguLi4jhmc86cOR8+fJg9e7a3tzdR214m/QIppULbfMm1IyXubUylFr6kqVemm8gwOe6jm6ROIWmfJsMilbQb4eiPFSXNH7LvES9qec1IRXfy0u/KuFtu2rRpjx49/vzzTz09PRMTEw8Pj5o1a44aNYr9/V/WH3/8oauru2HDhlItX7hw4fbt29OnT+/SpUvZ6ZbslRq/J7S0tDQ0NMQ46VWBLFf6ffr0iRAiPhayF8iy9/OwX5Hv3bvXxsbm/v377I3yT548adCggYmJSXp6+v3793Nycrp161byoYODQ7du3SIiIvLz8z08PLKyso4ePXrw4EFx4+IvhVNTU9mBgzh+oKBPnz6SWsvKyirZGtu4+NOL7OxsQoj45/a0tbW/fv3KfovNMExwcPDSpUvbtGljYWFRbvuGhoYuLi67d++2trb28/NLSEgQCASfP3+OiIgYMmQIx0upqamEEPY+bO5Z3rRp06VLl1q0aNGwYcOaNWvWr1/f0tKSvcRi165d06ZNY385sXbt2p07dyaEXLhwwdPT86+//vLy8iq7oDIzM8XLpCw2BvsjMKqwWqmQftUQQgoKCuLi4thrG3777TdfX187OztCiJWV1eHDh1etWuXj4xMZGVlQUJCcnPzgwYMuXbqUWnql7pcQc3Bw6N+//7Fjx3r37u3p6fn27dtnz54dPXqUz+evWrVq+vTp7MWfhJCNGzeOHj36p59+4k5barrSzybHvHAkkfRX7du3L3e7lYRjOyy5uZJ/b73kRx1B/P7MzMyaNWsqYUWzQ/eyUymZYd68eXp6euztHOXOZoMGDdzc3MzMzDp27Fi/fn3CubtjTyUzMzPr1KnDsVSpKLVA3r17R/7/QYSboaEhx5ZGJK8dDlLu6xS38EttrlL2R0lTf/XqlaRZqFCqUm+WNDnuA5+kTiFpn6atrS1pkXIodzfC0R8rdCbww70H6+vXr6TE8ZRj25C0QsudEe4NvuwKlf6w/sOuNG7cOD8/v6NHjxJC9PX1vb2979y5w5bBrJYtW+7fv9/Pz2/hwoVLly5lv6W8devW+PHjx4wZw47TyJLUKzV+Tyi+dWrmzJlKnjQoW8khKaQZyeTt27djx44lhLRq1So6Ovrjx4/sUN0tW7a8cOECwzCTJ0+uVauWnZ3dxYsXT58+bWho6OXllZOT8/jxY1NT09atW0dFRTEMU+rhly9fRo0aZWxsbGRk5Ofnxw5hGRMTw34ZPXXq1LS0tP3797OX5y5btox7jB1JrbGfoo0aNerly5dXr17t1KkTIWTAgAFPnjy5fv06+znKqFGjXr16xSYcPnx4v379AgICAgMDxSMsSWqfYZjMzMxx48Y1aNDA3Nx82bJlAQEB48aNu3jxItuTy33p0qVLPXr0IITY2NhcuHCBe5ZjYmKMjY1L/jxf06ZNk5KS8vPzu3XrNnDgwLVr1wYEBGzfvp3Nw34vf/z48bKL6MSJEy4uLoQQHo83f/78hISEkq/evn2bvSPL2tr677//VpHVquQx/S5fviz9qpk4caKOjs64ceO8vLwmTpy4fPly8eBC3759c3Nzq1mzpp2d3d27d8ePHz927NjTp0+XXVwccnNzp0yZ0rhx4wYNGkyZMiUzM1P80rFjx1xdXadNm7ZkyZL169ezg5Jzpy053QrNJse8SErCsQQkbbccyt0OY2JiSm6uZbdejo5w+fJl8e+6DBgw4NChQ4pe0SdOnOjevTs7xY4dO7q6urq6unbu3Jm9foZdCJK6GzuhSZMmldxgyu1lO3bsYO+hHzNmjHiwMm5EiWP6lVwgDx48YG92b9asWXh4eMltWxJJWxrH2pFE+n0d+365L/xSASrUHyUdicqdhQ0bNkifqtxZkOHAx9EpJO3TJE1FEkm7EUmTruiZAMfegyUSibZt28a2Wa1ateXLlycmJkqaEY4VmpubK2l/WO4GX3YdVfSwLqllVl5e3owZM8QPHz58WHIAQLE3b97MmTPHw8PD09Nz+PDhI0eOFB8RWJJ6pcruCeV4psEwzIYNG+rVq/fDHRGoOx5T4pvTyMhIdmj/ihVkoCz79u2rXbv2oEGDvnz58vnz55SUlMePH6enp69evZp2NGWQ4/bJXhtQ8uqUSvL399+/f39eXp68GgTVpAor2sbG5tq1a9WrV5dvszwe79ChQz4+PirSjgyUsHYUtPABNJXieqWCOqN8z4QfPXrUuXPnJ0+elDsmB2gM+Y/ppxympqbiL+JLCQsL69+/v5LzKMH9+/eDgoLYAUmNjIyMjIzatWtnbW0dERFBOxrIQRXcpMuF5cAtJibmp59+0uyzeblvA/JqUOaFr5pbtWqmKpcaRQXlUJc9YYcOHQwMDG7evIlqSrOpazVVauSWqiA+Pv7Dhw/BwcEDBw5s27Ztdnb2rVu3Lly4sHbtWtrRgGRkZBQWFubk5LA33sigCm7S5VLx5VD5FS2b2NjYyZMnW1paxsXFXbt2TZmTVj6ZtwFJa6eSG1XlF75qbtWqmapcahQVSpHvPlPt9oRaWlrW1ta3b98OCAignQUUSM4jpIPi+Pr6Ll68ePPmzdbW1sbGxm5ubhkZGSEhIdy/bAhKMH/+/HPnzolEohkzZggEAtpxQFEoruj69evn5+ffu3dv27ZthoaGypy0ulDc2sHCB5CN3HulOnZGW1vb27dv004BioX7ptTP9+/f9fT0VG34OyVQ5fumANSdBtw3BQBQSXI/Ez5+/Linp2dGRkbt2rXl1SaoGnw3pX709fWrYCkFAAAAoF5sbW1FItG9e/doBwEFQjUFAAAAACB/jRo1Mjc3v3XrFu0goECopgAAAAAAFAK3Tmk8VFMAAAAAAAqBakrjoZoCAAAAAFAIOzu7tLS0pKQk2kFAUVBNAQAAAAAoRJcuXXR0dPD1lAZDNQUAAAAAoBB6enodOnRANaXBUE0BAAAAACiKra3tnTt3aKcARUE1BQAAAACgKJ07d378+LFIJKIdBBQC1RQAAAAAgKJ06tQpNzf3xYsXtIOAQqCaAgAAAABQFCsrKx0dnUePHtEOAgqBagoAAAAAQFGqVatmYWGBakpToZoCAAAAAFCgzp07o5rSVPyyT3l7eys/ByhfRkaGtrZ27dq1aQeRVnJyshxbu3XrFjZ1BcnPz69evTrtFBRkZWVlZ2c3atRIS6tKf1D1n//8JyoqinYKUFHFxcWFhYV6enq0gwCUQ75nGiV17Nhx7dq1Cmoc6PpXNWVmZubl5UUrCijZy5cv371717BhQwsLCyMjI9pxfszU1FRe26e9vb1c2oGyRCLR1atXzczM2rVrRzuLsn369CkuLo7P55uampqZmRkZGfF4PNqhpOXl5WVmZiaXdirfCGgqhmHu3LmTk5PTp08f2lkAyiHHM41SOnXqlJaW9unTp4YNGyqifaCIxzAM7QxAjUAgWLNmzalTpzp37jxz5sxRo0Zpa2vTDgXqbc2aNcuXL09MTGzatCntLBSkpKQcPnw4Kirq+vXrJiYmXl5e3t7eDg4OalRWAShOUFDQH3/8ce7cuZ49e9LOAqBUX758MTQ0PHPmTL9+/WhnATmr0pejgKOjY3R09P379y0tLcePH9+6deuQkJC8vDzauUBdffr0aeXKlUFBQVWzlCKENG7cODAwUCAQJCUlzZgx4+zZs05OTs2bNw8KCvrnn39opwOgac+ePWvXrt25cydKKaiC6tevb2Zm9vDhQ9pBQP7w3RT816tXrzZu3Lhjx45atWpNmTJlxowZ9erVox0K1Iyvr29sbOzTp0/19fVpZ1EVCQkJ+/btCwsL+/jxY7t27fz8/Pz8/Bo1akQ7F4BSxcbG9unTZ+7cucHBwbSzANDh7u6up6d38OBB2kFAzlBNwb+kpaVt2bJl48aNRUVF48ePnz17trm5Oe1QoB5u3Ljh6Oh45MgRDw8P2llUjkgkunHjxr59+w4ePJiTk2Nvb+/t7T1q1ChDQ0Pa0QAU7vXr17a2tj169Dh06FAVH6MFqrIlS5ZERkY+e/aMdhCQM1RTUI6cnJxdu3b9/vvvnz59Gj58+C+//GJpaUk7FKg0kUhka2tbu3btixcv0s6i0goKCs6fPx8VFXXkyJHi4uI+ffp4e3t7eXnh2zzQVBkZGfb29gYGBlevXsV2DlXZ4cOHhw8fnpOTUzWHvdVg+IgIylGzZs3AwMCXL1/u3LnzwYMHVlZW7B1WtHOB6goNDX306NGGDRtoB1F11apVc3NzCwsL+/Dhw/bt2wkhEyZMMDEx8fPzi46OFgqFtAMCyFNRUZG3t3dBQcGpU6dQSkEV17Zt2+Li4ufPn9MOAnKGagok0tXV9fPzi4uLO3nypJ6e3uDBg62trcPCwoqLi2lHA9Xy9evXJUuWBAYG4jtM6dWuXZutoN6+fbt8+fLXr1+7u7s3bNhw0qRJAoEAVw2AZpg2bdrdu3dPnjzZoEED2lkAKGvdurWOjk5iYiLtICBnqKbgB3g8npub24ULF+7du9e+ffvx48dbWFhg6D8oaeHChdra2osXL6YdRC2ZmJiwwwC+fv167ty5V65ccXJyatasWVBQED7CBLW2cuXKXbt2RUREdOjQgXYWAPp0dHRatmyJakrz4L4pqBh26L/t27cbGBhg6D8ghMTHx3fu3Hnnzp1jxoyhnUVDlB0GcPTo0SYmJrRzAVTAkSNHfHx8QkJCpk2bRjsLgKrw9vYWiURHjhyhHQTkCdUUyCI1NXXr1q3iof/mzJljZmZGOxTQ0aNHj9zc3Nu3b2OoLvlihwGMioqKiIhg7+PHMICgLu7fv+/i4jJhwoSQkBDaWQBUyNKlSyMjI58+fUo7CMgTqimQXXZ29l9//bV+/frU1NThw4fPmzevffv2tEOBUkVERIwePfrmzZs2Nja0s2iscocB9PT0rFGjBu1oAOVISUmxtbW1srKKjo7m8/m04wCokIMHD44ePTonJ6datWq0s4DcoJqCyiosLDx48OCaNWuePn3aq1evGTNmuLm50Q4FypCTk9OmTZsBAwawY9OBon379u3EiRNRUVFnz56tUaPG4MGDvb29+/fvjxNWUB3Z2dlOTk5FRUU3btyoXbs27TgAquXJkycdO3aMi4vDoE2aBFfmQGWxQ//Fx8efOHEiLy8PQ/9VHb/++uv3799/++032kGqCvEwgJ8+fVq7di2GAQRVU1xcPGrUqNTU1DNnzqCUAijLwsKCz+djIAoNg2oK5IMd+k8gEGDovyri5cuXGzZsWL58uZGREe0sVU79+vUDAgIEAkFSUtLcuXOvXr0qHgbw2bNntNNB1TVr1qyLFy8eO3bM3NycdhYAVVStWrXmzZujmtIwuNIPFOLly5d//vmneOi/wMDAunXr0g4F8jRw4MC3b98+fPhQR0eHdhYoPQygt7f32LFjmzZtSjsXVCE7duyYNGlSeHj4iBEjaGcBUF1Dhw7V1taOioqiHQTkBtUUKBA79F9ISEhxcfG4cePmzp1rampKOxTIwalTp9zc3GJiYnr27Ek7C/wPhgEEWs6dOzdo0KAVK1bMnz+fdhYAlRYUFHT27NlHjx7RDgJyg2oKFE489F9aWtqwYcOCgoLatWtHOxTIrrCw0MrKqnPnzgcPHqSdBconHgbw6NGjQqEQwwCCQiUmJjo4OLi7u+/Zs4d2FgBVt3PnzpkzZ2ZnZ/N4PNpZQD5w3xQoXK1atQIDA1+9erVjx4579+5ZWlq6ubldv36ddi6Q0fr169+/f79mzRraQUCiatWqubm5hYWFpaSksCMuTpgwwdjY2MfHJzo6uqioiHZA0Bzp6emDBw+2srIKDQ2lnQVADbRq1So3N/fTp0+0g4DcoJoCJSk59F9GRoajo2PXrl0x9J/aSUlJWbVq1cKFC5s0aUI7C/xYyWEA//Of/3z48MHd3b1Ro0YYBhDkIj8/f/DgwQzDHDlyBL+fAyCNli1bEkJevHhBOwjIDaopUCotLS32i6nY2NhGjRqNHTu2TZs2ISEh+fn5tKOBVObMmWNsbDx79mzaQaBiSg0DeO3aNScnp6ZNm2IYQJAZwzATJkx4/vz5mTNnMLYngJRMTExq1Kjx8uVL2kFAblBNAR2Ojo7R0dHPnz8fMGBAUFBQ06ZNly1b9vXrV9q5gItAIDh06NCGDRuqV69OOwvIqEmTJvPmzXv69Gl8fPy4ceMiIyPbtm3bvn37ZcuWJSUl0U4H6mTx4sVRUVGRkZGtW7emnQVAbfB4vBYtWqCa0iQYhQLo+/Tp07Zt2zD0n4orLi62trY2NjY+f/487SwgN+UOAzhy5Eh81QDcDh48OHLkyG3btgUEBNDOAqBmPD09tbW1IyMjaQcB+UA1BaoiKytr9+7d69at+/z5M4b+U0GbNm2aPXv2kydPLCwsaGcB+cMwgCA9gUDQu3fvWbNmrVq1inYWAPUzb968CxcuPHjwgHYQkA9UU6BaCgsLDx48uHr1avYiwPnz53fv3p12KCAZGRmtW7eeMGEChvLTeN+/fz99+nRYWNi5c+f4fP6gQYNGjx7dr18//EwzsJKSkmxtbZ2cnKKiorS0cL8AQIXt2LHj//7v/7KysjBIumZANQWqSCQSnT59etWqVTdv3nRwcAgMDGR/O5x2rqpr0qRJJ0+efP78uYGBAe0soCRfvnw5cuRIWFjYjRs36tat6+XlNXr0aAcHBxz+q7KsrKzu3btXq1bt2rVr+N4SQDZXrlzp2bPnx48fGzZsSDsLyAE+VQJVxA79d+PGjdjY2Lp16w4bNgxD/1H08OHDXbt2rVu3DqVUlSIeBvDNmze//PKLeBjAwMDAhw8f0k4HFBQVFQ0dOvTr168nTpxAKQUgsxYtWhBCXr9+TTsIyAe+mwI1EB8f/+eff+7du7dOnTqTJ0+eOXNmnTp1aIeqKhiGcXFxKS4uFggE+FKiiktISIiKigoLC0tKSmrXrp23t7efn1/z5s1p5wIlmTx5cnh4uEAg6NixI+0sAGqsuLhYT09v7969I0aMoJ0F5ADfTYEasLS0DA0NffPmzeTJk0NCQszNzQMDA1NSUmjnqhL27dt3/fr1DRs2oJQCdiD1ly9fxsbG9u7de8uWLa1atXJ0dAwJCfn8+TPtdKBYa9eu3bFjR3h4OEopgErS1tY2NTV9+/Yt7SAgH/huCtQMO/Tf2rVr09PThw0bNn/+/LZt29IOpbGys7PbtGkzePDgrVu30s4CKqfkMID5+fk9e/YcPXr00KFDa9asSTsayNnp06fd3d3/+OOPGTNm0M4CoAl69uzZpk0bHFs1A76bAjVjYGAQGBiYlJS0Y8eOu3fvWlpasndY0c6lmVasWJGXlxccHEw7CKiiatWqubm5hYWFff78+cCBA9WrV584caKxsbGPj090dHRRURHtgCAfDx48GDZs2Lhx41BKAchLkyZN3rx5QzsFyAeqKVBLurq6fn5+CQkJx48fT09Pd3BwcHR0jI6OxnetcvTixYs///zz119/NTQ0pJ0FVJqenp63t3d0dPSnT582bNjw4cMHd3f3hg0bTpo0SSAQoFeqNXZtOjo64kN0ADlq0qQJrvTTGLjSDzSBQCBYs2bN6dOnW7Zs+fPPP0+aNKl69eq0Q6m9AQMGvH///uHDh3w+n3YWUDPv3r07duzY3r17Hz58aG5uPmTIkLFjx3bu3Jl2LqiYnJwcJyengoKCGzduYOwfADn666+/pk2blpubi3uSNQCqKdAccXFx69atO3jwYP369SdNmoSh/yrj+PHjHh4eV65ccXFxoZ0F1Bg7DOC+fftev36NYQDVi0gkGjp06PXr12/dusUO6AwA8hITE9OrV6+0tDQjIyPaWaCyUE2Bpvn48WNoaOiGDRsYhhk7duwvv/zSuHFj2qFU3eHDh62trZs1a8Y+LCgosLKy6tatW3h4ON1goBlEItGNGzeioqIOHDjw5csXe3t7b2/vESNGGBsb044GhBDCMEzZD8hnzZq1bdu2mJgYe3t7KqkANNirV69atmx5584dc3PztLS05OTk1NRUd3f3unXr0o4GFYZqCjRTqaH/FixY0KZNG9qhVJejo+OdO3fmz58/b948fX394ODg1atXP3361NzcnHY00CjFxcWXL18OCws7duxYXl4ehgFUEVOnTnV0dBw5cqT4mV27dvn7++/bt2/UqFEUgwFokgMHDrx48eLTp0/v379PTk5OTEwsLi4uLi5mX9XX18/MzNTR0aEbEmTBAGiu/Pz8vXv3WlhYaGlpDRo06MaNG7QTqaLi4mJ9fX1CCJ/Pb9So0bZt22rUqLFq1SrauUCTff/+PTIyctCgQTo6OuwgFidPniwsLKSdqyrKzs7W09MjhPz222/sM+fOnePz+cHBwXSDAWiYwMBAQki59RKPx+vZsyftgCAjjOkHmqxatWp+fn6JiYns0H/du3fH0H9lvXjx4vv374QQoVCYmpo6ZcoULS2tPn360M4FmkzSMIB+fn4XL15ED1WmyMjIwsJCQsjixYv9/PyePHkybNgwT0/PhQsX0o4GoFHmzJnD5/PL/fUIHR2dn376SfmRQC5QTYHm09LScnNzu3nzZmxsbN26dd3d3Tt27BgWFsb9ezjbt29nzzA03v3797W0/rsrEIlEDMPk5eXZ2NgEBAR8+fKFbjbQePXq1QsICBAIBG/fvl2yZEl8fHyfPn2aNGkSGBj44MED2umqhNDQULZ8FYlEBw4c6NWrl4WFxd69ezHUGIB8mZqaDhs2rNzvpgoLC52dnZUfCeQC1RRUIewXU48ePerUqdPEiRPNzc2XLVv27du3su/8559/pkyZMmTIkPz8fOXnVLKyY6ALhUKRSLR79+5WrVr99ddfIpGIVjaoOszMzNgKKj4+fvz48adOnbK2tm7fvv2yZctevXolTQtHjhz58OGDonNqmOfPn9+9e1fcx4VCYWZmZkpKSlJSEt1gABpp4cKFQqGw7PM6Ojo2NjbKzwNygWoKqpwOHTqEhYW9ePHCx8dn/fr15ubmgYGBpU7C1q1bp6WldeHChYEDB7JXwWmwW7dulfstnFAo/Pr167Nnz5QfCaoytoJ68eJFbGxs7969t27d2rJly65du4aEhKSlpXH84fz58zt27Hj9+nWlRdUAu3btKvthyqdPn2xsbC5fvkwrFYCmatu2bd++fcv+imPXrl3xO5nqC2P6QZX27du3PXv2rFmzJiMjw8fHhx36LzU11czMjL0OkM/nd+vW7dy5c7Vq1aIdViEYhjEwMMjJySn1vJaWFo/H+/PPP6dMmUIlGABLymEAHz161LlzZy0tLS0trU2bNk2aNIlWYDVSVFTUqFGjci/oZZdkaGjo+PHjlR8MQIPFxsaWuqhPV1d33rx5K1asoBUJKgnfTUGVVrt27cDAwKSkpO3bt9+5c6d9+/Zubm6//PKL+A1CofDevXsuLi4ZGRkUcyrOy5cvy5ZSfD6/Zs2aFy5cQCkF1Glra/fu3TssLCwtLe3AgQPVq1efOHGisbGxj49PdHS0+O7HiIgIHR0dkUgkFAqnTJni6+ubl5dHN7nq+/vvvyXt2bS1tRmGSUhIKPeqJACQmZOTk42Njba2tviZwsJCJycnipGgkvDdFMB/iUSi06dPr1ix4tGjR6VOIPh8ftu2bS9fvly/fn1a8RTk4MGDI0eOLLkf4PP5jRs3Pnv2LH6hC1RTRkbGqVOn9u3bd+nSpTp16gwaNGj06NGjR49OTU0Vv4fP57dv3/7kyZP4zTQOAwcOPH/+fKndnZaWlkgkcnZ23rx5s6WlJa1sABrsxIkTQ4YMET/U1tb++vWrpl4CUxXguymA/2KH/hs2bFjZjxiEQuGzZ89cXFw+f/5MJZviPHjwoOT4Qnw+v2vXrnfv3kUpBSqrXr16fn5+Fy5ceP369Zw5cx48eODq6lqylCKECIXCxMTETp06XblyhVJMVZeamnru3LlSpZS2traxsfHevXuvXr2KUgpAQQYPHty6dWvxaLodOnRAKaXWUE0B/E9RUdHvv/8u/mHyUi/9888/9vb2GjZo2O3bt8VDUGhpafn6+hsuOBAAACAASURBVF69etXIyIhuKgBpNG3adMGCBfHx8V5eXrq6uqVeLSoqysrK6t2798aNG6nEU3F79uwpOQa6rq5utWrVFi1alJSU5OfnRzEYgMbj8XhBQUHs/3V1dXv37k03D1QSqimA/9m/fz/HoGFFRUXv3r1zdHRMTk5WZirFYRjm4cOHhBAej8fj8RYvXrx79+6yZ6UAqqywsPD8+fPljktZXFxcXFw8c+ZMf3//KvLzcdLbsWMH+8UUe/9G7969nz9/vmzZMgwsBqAEo0aNMjQ0JLhpSiOgmgL4L4Zh1q5dy30nYVFRUXJycvfu3d++fau0YIqTlJSUnZ3N4/GqVat2/PjxZcuW0U4EUGFnzpzJysrieAPDMHv27HF0dPz48aPSUqk4gUDA/oqXlpZW69atr169evr06SZNmtDOBVBV6OrqskNe8Xg8BwcH2nGgUkoPeA9V082bN9+/f087BWXZ2dmWlpampqYZGRkZGRlfv37Nzs4W/6illpYW+wmuUCh8//59p06dli9f3rBhQ6qRK+vWrVuEEAMDgwULFuTn50dGRtJOBOUwMzOzt7evfDuaun43bNjA4/1gRCWhUMjeDfjLL7+0atVKadlU1tatWwkh+vr6I0aM6N2796dPnzR181Aj6OlVjaGhYfXq1evXr3/x4kXaWaBiSvdWBoBhvLy86G2TAMDFy8tLLt2c9nwAABf0dAB1Uaq34rsp+C8vL6+oqCjaKdSMUCgs+4vmamT79u2+vr76+vq0g4BE3t7ecmzt0KFDPj4+cmwQ1NHjx495PF6HDh1oB4H/QU+vmtLS0u7duzdgwADaQaACyvZWNT4RBKBOrUspQkhAQADtCACgbB07dqQdAQAIIcTY2BillAbAKBQAAAAAAACyQDUFAAAAAAAgC1RTAAAAAAAAskA1BQAAAAAAIAtUUwAAAAAAALJANQUAAAAAACALVFMAAAAAAACyQDUFAAAAAAAgC1RTAAAAAAAAskA1BQAAAAAAIAtUUwAAAAAAALJANQUAAAAAACALVFMAEmVnZyuuhco3LjOKkwaAH5JLD0U3B1AOTT1VAOmhmgIox+bNm52cnOzs7GRuITQ01MXFpW3btj98KSYmxs7O7s2bNzJPSy6paPn27duiRYucnZ2trKzc3Nw8PDwWLFiwYMGCTZs20Y4GKkooFMbGxi5cuPDcuXPsM8rsRApV+T0PQTeHKkBFurxGniqgt8oA1RSok48fPypnQpMmTfr27ZtIJJK5hYkTJ4pEouLi4h++9PXr1/fv3+fm5so8LbmkoiI6OrpNmzZXr14NCwuLi4uLjo7+66+/Pnz4sGrVqu/fvyszieI2LaVttJpBmsV19+7d3bt3r1y5Mjk5mX1GmZ1IoSq/5yHo5pKhm6uOSi4xFenymneqgN4qG1RToDYyMzNHjhypnGnx+fzGjRtXpgVtbW1TU1NpXvL09ExJSWnfvn1lJlf5VMoXGxvr6enZtGnTmJiYpk2bsk/WrVt3z549I0eOVOZhUnGbljI3Wg0g5eKyt7efPn16yWeU2YkUqvJ7HoJuLgG6ueqo/BJTkS6vYacK6K0yQzUF6iE3N9fHx4f61/ogRzNmzCgqKgoODtbR0Sn10vLly5X2MZjiNi1stBVSocWlq6ur4DggH+jmUAqWmMpCb5UZqimomL///nvq1KmBgYH29vY7duwQP3/48OHp06fPmTOnf//+ixYtKigoIIQ8evRo7ty5zZs3z83NnThxoqGhoY2NzevXr7lbS01N9ff3Dw4O9vf39/Dw+PLlCyHk2LFjz549S09P9/f3X79+PXdIhmG2bds2ZcoUW1tbV1fXFy9eEEISEhIWLFhgYWGRnJy8bNkyc3Pz9u3bX758OT8/f9asWS1atDA3NxffhiF26dIlV1fXevXq9e3bV5y83PZZJ06cCAgImDdv3owZM0p9myzppc+fP2/atOn27dvSLLF79+75+/uPHDnSxsYmNDRUKBT+aI1xTbrcGeHO8Pjx47Fjx65du3bWrFlTp07lXiACgcDMzOzMmTNlI8XFxT169KhOnTq9e/cu+2rLli3FXz4of9OSYbFUpmVVoxZ9vBTpO5GkVVBupJSUlNWrV1taWmZkZPTt27dJkybs85Ioes/DDd2cfRLdXErq0tPLbblCx01JquCpAnqronorA8AwXl5eXl5eP3xbWFjYiBEjiouLGYb57bffCCGXLl1iGOaPP/5wcHAoLCxkGCY9Pb1Vq1bOzs4ikejjx49st/z5558TEhIePnxYrVq14cOHc7fWo0ePYcOGse/p2LGjr68v+/9BgwY1bdpUmtlZtWrVnj17GIYRCoXt2rVr2LBhbm5uWlra6NGjCSEBAQH379/PysqytbVt3rz5tGnTEhMTs7Ozu3fv3rx5c3Ej/fr1q1+//vjx48+ePbtlyxZ9fX0TE5OcnBxJ7TMMEx4ebmdnl5eXxy4HIyOjhg0bsq1JekkgEDg5ObG7J4ZhuJfYmzdvatSokZSUxDCMn58fIcTa2nrmzJncS4MjVbkzwp3BwsJCIBAwDPP9+3cnJyeOdhiGOX36tJ6eXnh4eNlUO3fuZPNzh6eyacmwWCrTMvcSkLJvSoMQcujQIe73qEsfj4+PJ4Ts3LmTqWAnkrQKyo105syZNm3aaGtrL126dPv27TY2NikpKRypFL3n4YBurtbdnEFPr0jOCnV5DlXwVAG9VUG9FdUUMIx0+/G0tLTatWu/fv1a/HDo0KGJiYmpqak1atTYt2+f+J27d+8mhISFhTEMM3/+fEJIeno6+5Kjo2OrVq04WmMYpkePHitXrmSfHzVqVIcOHdj/S7n/TUlJadCgAdtpGYZZsmQJIeTgwYMMw2zevJkQ8uTJE/alpUuXEkIePnzIPly8eDEhJC0tjX3Yr18/ExMTcbOLFi0ihISEhEhqPzc3t1GjRhEREeI/GTp0KLsf5HiJYZjz58+Ld5EcS4xhmDlz5piZmbH//+effwghoaGh3EuDY9IcC0pShsLCQkLIn3/+yT7PNsvRDsMwQqGw3GDr1q0jhLi6unKEp7JpybBYKt8yB2WeY6lLH2f+XU0xUncijlUgKdKECRMIIS9fvvxhJEXveTgmjW6u7t2cQU+vSE6mIsdNSarsqQJ6qyJ6K1/yt1YA/yIQCEQiUbNmzdiHRkZGR44cIYScPHkyNzfXzMxM/M5BgwYRQq5cuTJ69GhtbW1CCJ//3y3N1NT05cuXHK0RQi5fvkwIycvLCw8Pv3PnDsMwFcp548aNoqKiSZMmiZ+ZOHGinp4eIYQNo6WlJQ5DCBFfH2xubk4IYT8NYp8xMDAQNzJu3Lhff/31/v37JiYm5bYfGxv78eNHKysr8fPiWzs4XiKE6Ovrl8wvaYkRQlJSUsQXLrdq1crQ0PD9+/fcS4Nj0j9cUGUz6OjouLq6BgYGJiYmrly5csSIEdztiJsqi13aSUlJHOFv3bql/E1LhsVS+ZZVhLr08bKk7EQcq0BSJB0dHT6f36JFix9mUPSeh2PS6Obo5hWiLj2do2Xpj5uSVNlTBfRWaVquKFRTIK34+PiioiKGYXg8Xsnn3759SwjJyMgQP2NoaKivr//hwwcZWiOEFBcXr1279uHDh9OmTbO1tb1161aFcj59+rRGjRolrwKXpNSk2YeShjpt2rSprq5uXl6epPZDQkJIiR1uSc+ePZP0UoUMGDDgwIEDly5d6tWrV2ZmZk5OTr9+/bj/hGPS0i+oko4ePerv779169YjR45ERUU5OzvL1g47MFFSUpJQKBTvCkuhsmnJNjuKa1mZ1KWPy4xjFVQ+kqL3PBwNopuXhG7+Q+rS0zlarjycKpSC3loZGIUCpGVgYJCfn5+YmFjyyYKCAvbzhrJ3fFpYWMjQmkgkGjBgQFxcXGRkpLOzsww59fX1k5OTxT9Ew/r8+bMMTZWkpaXF5/MtLS0ltc9+hsTua0rheKlCfH19t2/f7ufnt3jx4tmzZx84cMDBwYH7TzgmLduC0tHRiYiI2LdvHyHE1dX12bNnsrVjYWHRunVr9mdYJb2HyqYlw+wormUlU5c+LjNJq0AukRS95+F4D7o5C91cSurS0yW1LENTZeFUoRT0VmlalgTVFEira9euhJDFixeLP5K5f//+gQMH7OzsatWqdfz4cfE72W+ZBw8eLENrd+7cOX/+fK9evdgn2c8z2P9raWnl5OT8MKeVlRXDMPPmzRM/8+rVqy1btkg/p+V68+ZNUVGRj4+PpPY7dOhACImKihI/L/7dPY6XKiQ/P/+ff/55/PhxcHDwrl27hgwZ8sM/4Zi0DAuqoKBg69athBBfX99bt26JRKKYmBjudiR9gMfn80NDQwkhCxYsYO/TKOnTp0979+6lsmnJsFgU17KSqUsfl5mkVcARqfKNVzKzeM/D8R50c4JuXhHq0tMltVyReZWoyp4qoLcqorfiSj+QloODQ//+/Y8dO9a7d29PT8+3b98+e/bs6NGjfD5/1apV06dPZ79WJoRs3Lhx9OjRP/30EyHk27dvhBDx6JypqansYDWSWmNvD9i7d6+Njc39+/fZ+2KfPHnSoEEDExOT9PT0+/fv5+TkdOvWrdQ1xGJ9+vTp1q1bREREfn6+h4dHVlbW0aNHDx48SAjJysoqGYbNJv4cIjs7m5T43EtbW/vr16+5ubk1atRgGCY4OHjp0qVt2rSxsLAot31DQ0MXF5fdu3dbW1v7+fklJCQIBILPnz9HREQMGTKE46XU1FRCSHp6eslUZZcYj8fbtGnTpUuXWrRo0bBhw5o1a9avX9/S0pL7qgAHBwdJk3Z3d5e0oCRlIITs2rVr2rRp7K8K1q5du3PnznZ2dpLauXDhgqen519//eXl5VU2W48ePbZs2TJnzpwePXqEhIR069aNEJKZmXnu3LkdO3aEhYUZGhoqf9NycHCo6GJhr16oTMsqQl36uHii4l+TlLITSdo5vHr1SlKknJyc4uLizMzMOnXqcC89Re95uFccujm6ufTUpadLaplI3eU5LhGsmqcK6K2K6q0/HLkCqgIpRxPKzc2dMmVK48aNGzRoMGXKlMzMTPFLx44dc3V1nTZt2pIlS9avXy8SiRiGiYmJYb8Xnjp1alpa2v79+2vUqEEIWbZsmVAolNTa5MmTa9WqZWdnd/HixdOnTxsaGnp5eeXk5Dx+/NjU1LR169ZRUVHcOb98+TJq1ChjY2MjIyM/Pz92UOOYmBj2g59Ro0a9fPny6tWrnTp1IoQMGDDgyZMn169f79KlC/vqq1evGIZ5/Pjx8OHD+/XrFxAQEBgYKB5IR1L7DMNkZmaOGzeuQYMG5ubmy5YtCwgIGDdu3MWLF9mzsXJfunTpUo8ePQghNjY2Fy5c4F5iMTExxsbG4ltjCSFNmzZlR0HlwJFK0oKSlCE3N7dbt24DBw5cu3ZtQEDA9u3buRdITExMo0aNjh8/zhHv5cuXAQEBtra2JiYm3bp1c3Fx2bp1K/tJEq1Nq6KLRSgUVqZlbkoeN1kt+vjt27fZ2wCsra3//vvvy5cvS9+JJK2CciNt2LCBvdF8zJgx4hG9OCh6z8MB3VytuzmDnl6RnBXq8hyNV8FTBfRWBfVWHlPpwZRAA3h7e5N/f78MKmjfvn21a9ceNGjQly9fPn/+nJKS8vjx4/T09NWrV9OOBooix77J4/EOHTrEfc0YAFCBng7yglMFRSvbW3GlH6glU1NTSbeihoWF9e/fX8l5lOD+/ftBQUEpKSmEECMjIyMjo3bt2llbW0dERFTBpQEaTzW3aoqpVHOBAFSSQjfsKthrOE4VaEfTZKimQC2VGoOlKoiPj//w4UNwcPDAgQPbtm2bnZ1969atCxcurF27dtq0abTTAciZavZxiqlUc4EAVJJCN+wq2Gs4ThVoR9NkGNMPQD34+vouXrx48+bN1tbWxsbGbm5uGRkZISEhqva7kAAAAEAFThWowHdTAOpBW1t7xYoVK1as+P79u56eniJ+zRAAAADUF04VqEA1BaBmOIaNBgAAAMCpgjLhSj8AAAAAAABZoJoCAAAAAACQBaopAAAAAAAAWaCaAgAAAAAAkAWqKQAAAAAAAFmgmgIAAAAAAJAFqikAAAAAAABZoJoCAAAAAACQBaopAAAAAAAAWaCaAgAAAAAAkAWqKQAAAAAAAFmgmgIAAAAAAJAFn3YAUBXJycmRkZG0UwDAvyQnJ5uamsqrtZs3b8qrKQCQI/R09cIwDI/Ho50C6CintzIADOPl5UVpmwSAH/Dy8pJLN6c9HwDABT0dQF2U6q08dDyAKm7KlClRUVGJiYnGxsa0swCAVCIjI4cNG4YjOICSLVu2bMWKFb///vusWbNoZwFVgfumAKq6NWvW6Ovrz549m3YQAAAAFcUwzMyZM3/99dedO3eilIKSUE0BVHUGBgbbtm3bv39/dHQ07SwAAAAqRygUjhs3buvWrYcOHRo/fjztOKBaUE0BABkwYICPj8+0adOys7NpZwEAAFAhBQUFPj4+hw8fjo6O9vT0pB0HVA6qKQAghJDNmzfn5eUtXLiQdhAAAABVkZOTM2jQoMuXL58/f97V1ZV2HFBFqKYAgBBCDA0N161bt3nz5uvXr9POAgAAQF9GRkafPn3i4uKuXLnSvXt32nFARaGaAoD/GjNmTJ8+fSZOnJifn087CwAAAE0fP37s0aPHx48fY2NjO3bsSDsOqC5UUwDwP6GhocnJyatXr6YdBAAAgJqkpCQnJ6eioiKBQNCqVSvacUCloZoCgP9p0qTJihUrVq1aFR8fTzsLAAAABYmJiU5OTnXq1Ll27ZqpqSntOKDqUE0BwL8EBgZ27dp1woQJxcXFtLMAAAAo1d27d52dnVu0aBETE2NkZEQ7DqgBVFMA8C9aWlrbtm179OjRpk2baGcBAABQnitXrvTq1cvOzu7s2bMGBga044B6QDUFAKVZWVnNmzdvwYIFr1+/pp0FAABAGaKjo/v37z9w4MBjx47p6enRjgNqA9UUAJRj0aJFzZo18/f3ZxiGdhYAAADFCg8PHzp06NixY8PDw3V0dGjHAXWCagoAyqGrq7tr164rV67s37+fdhYAAAAF2rJli5+f3+zZs7ds2aKlhXNjqBhsMQBQPltb2ylTpsyaNSs1NZV2FgAAAIVYs2bNtGnTVq5cuXr1ah6PRzsOqB9UUwAg0Zo1awwMDGbNmkU7CAAAgJwxDDNnzpz58+dv2LBh3rx5tOOAukI1BQAS1ahRY/PmzQcOHDhx4gTtLAAAAHJTXFwcEBAQEhKye/fuGTNm0I4DagzVFABw6d+//8iRI6dOnZqZmUk7CwAAgBwUFhaOGDFi3759UVFRY8aMoR0H1BuqKQD4gY0bNwqFwoULF9IOAgAAUFnfv38fPHjwmTNnTp06NWTIENpxQO2hmgKAH6hfv/7vv/++bdu22NhY2lkAAABkl5mZ6erqevfu3YsXL/bu3Zt2HNAEqKYA4Md8fX0HDhw4ceLE/Px82lkAAABkkZqa2rNnz9evX1+5csXW1pZ2HNAQqKYAQCqbNm36+PHjb7/9RjsIAABAhb17987Z2fnbt2+xsbFWVla044DmQDUFAFIxNzf/7bffVq9e/fDhQ9pZAAAAKuD58+eOjo58Pl8gELRo0YJ2HNAoqKYAQFo///yzra3tpEmTiouLaWcBAACQyoMHD5ydnRs2bHjt2jUTExPacUDToJoCAGlpaWnt3LkzLi4uJCSEdhYAAIAfi42N/emnn9q1a3fp0qX69evTjgMaCNUUAFRAmzZtgoKCFi9e/OrVK9pZAAAAuFy6dGnAgAHOzs5nzpypVasW7TigmVBNAcD/Y+/eA2JK/z+APzNNqFAoJRWicr+XEFqRRFqiXCqicku0Wta9lUuLxbrkuqwoSmTtYpXKKjaXlpLQuuRSbfek6TaX8/vjfH/z7dtlGtM0Z6r366+Z58w8533OnDnNp3POc77MunXrDA0NPTw8KIpiOgsAAEDtrly5MmXKFHt7+8uXL7dp04bpONBsoZoCgC/TqlWrn3/++c6dO2fOnGE6CwAAQC2CgoJmzZrl7u4eFBTE4XCYjgPNGaopAPhiZmZmXl5eq1atyszMZDoLAADA/zhw4MCCBQtWr1596NAhNhu/daFxYQsDAGns2LGjU6dOq1atYjoIAADAf/3www+rVq3avXt3QEAA01mgRUA1BQDSUFVVPXHiRHh4+JUrV5jOAgAAQCiK8vHx2bBhw4kTJ1avXs10HGgpUE0BgJTGjx/v7Oy8bNmyoqIiprMAAECLJhAIFi5cGBgYeP78+UWLFjEdB1oQVFMAIL19+/YJhcLvvvuO6SAAANByVVRUODo6hoWFXb16ddasWUzHgZYF1RQASK9Tp0779u07fvx4dHR01XaKooqLi5lKBQAALQeXy7Wzs4uJiYmMjJw0aRLTcaDFQTUFAA0yZ86cadOmLV26tKysjG5JS0sbO3bs8ePHmQ0GAADNiVAorNlYWFg4ceLE5OTk2NjY0aNHyz8VAKopAGiowMDA3Nxcf39/Pp8fEBAwYMCA+Pj427dvM50LAACaCYqiJk2alJqaWrXx33//tbS0zMjIuHPnzuDBg5nKBi0cbmcGAA2lq6u7ffv2b7755urVqy9evBAIBISQuLg4oVCIG30AAEDDhYaG3rp1a/z48ffv3+/WrRshJD09feLEiRwOJz4+Xl9fn+mA0HLhhw4ANFRZWVl2djafz3/58iVdShFCiouLq/0TEQAAQAp8Pn/9+vVsNjs/P3/cuHHZ2dnPnz+3sLBo3779nTt3UEoBs3BsCgAaJC4ubsGCBe/fvxfVUTQOhxMXF9e/f3+mggEAQPNw+vTpd+/eCYVCoVCYmZk5cuTIoqKi/v37//bbb+rq6kyng5YOx6YAQEp8Pt/T03PcuHHv3r3j8/nVplIU9eeffzISDAAAmo3y8vLNmzdTFEU/5fF4Hz9+5HA44eHhKKVAEaCaAgApcTicSZMmqaqq1npxlEAgiImJkX8qAABoTo4ePZqbmyuqpgghPB6vqKjIycmpoqKCwWAANFRTACA9BweHx48fGxoacji1nDacm5v79u1b+acCAIDmgcvl+vv7VzuTnBDC4/Hi4+NnzZpV88wIADlDNQUADWJkZPTo0aMZM2awWKxqk5SUlO7cucNIKgAAaAb2799f173g+Xz+b7/95unpWfWwFYD8oZoCgIZq27ZtaGjo0aNHORyOkpKSqJ3NZsfFxTEYDAAAmq7CwsKAgIBajz7Rf2v69ev31Vdf1TxyBSBPqKYAQDY8PT1jY2M7dOigrKxMt/B4vOjoaGZTAQBAE7Vr167y8vJqja1atWKxWGPHjr169WpKSoqLi0utp5oDyA2qKQCQGQsLi9TU1FGjRomOUKWnp2dlZTGbCgAAmpx///13//79VQ9McTicNm3aLFiwIDU1NSYmxs7OjsF4ACKopgBAlrS0tG7durVixQpCCH0lVXx8PNOhAACgidm+fTt9Cp+SkhKLxdLS0tqwYUNGRsaxY8d69+7NdDqA/0I1BQAyxuFw9u3bd+XKFVVVVYJqCgAAvlB6evrRo0fpA1ODBg26cOFCZmamn59fx44dmY4GUB0LA6GA4qg5KBwAtEChoaGOjo5Mp1BoYWFhTk5O+AsOVeFv6MyZMy9evMh0CmhxcN0eKJZVq1aNHDmS6RQgM+Xl5ceOHXN3d1dTU2M6CzQNTk5OTEcAaKqax9/QwsLCP/74Y+LEiZqampK/a9++fY0XCUAMVFOgWEaOHIn/STczLi4ulZWVrVu3ZjoINA2opgCk1mz+hi5evPhL34KjUsAUXDcFAI2LxWKhlAIAAIBmCdUUAAAAAACANFBNAQAAAAAASAPVFAAAAAAAgDRQTQEAAAAAAEgD1RQAAAAAAIA0UE0BAAAAAABIA9UUAAAAAACANFBNAQAAAAAASAPVFAAAAAAAgDRQTQEAAAAAAEgD1RQAAAAAAIA0UE0BAAAAAABIA9UUND2fP39mOkITJpO116I+gpKSEqYjAAAAgIJCNQVNyeHDh8eMGWNubs50kCZJJmvv2LFj48aN69Onj6xSSS0yMtLV1ZXFYrFYrJEjR1pZWY0cOXLEiBH79u2TVf0THBw8YcIEIyOjmpMuXLjQr18/Fos1ZswYPp8vai8sLPT392/fvr2amtqmTZvy8/NlkkRyfD4/Li5uw4YNN2/epFtiYmLMzc3T09Nl0r9sewMABSHbr/anT582btw4duzYAQMG2NnZTZ8+ff369evXrz906NCtW7cWLVpE77ptbGxCQkIUdncKICkKQGEQQkJDQ8W8gMfjDRgwoHfv3nKLJAeZmZnymZFM1h6fz7ewsNDR0ZFVqoYQCARt27YlhAgEArrl3LlzbDbb0tKysrKy3rfXu+b5fL6lpaWmpmatU3NycjgcDiFkzZo11SatXLly+fLlEiyB7N27d8/NzY0QcvLkSbolPDxcV1c3JSVF6j6rrqiG91avevcDQFFUaGgo/oJDNQ357sjwq3316lUdHR0LC4u3b9/SLQUFBfPnzyeE/PDDD3SLpqYmIeT9+/f0U5nsTmfOnDlz5syG5wf4Ujg2BU0Jh8Pp2rUr0ylkqaioaO7cufKZl0zWnpKSkp6enkzyNBybzVZXV6cf0C3z5s1zcHC4ffv23bt3xb9XkjUvfmG1tLS0tbXV1dV37979+++/V53Uo0cPQ0NDiZZB1kaOHLlixYqqLQ4ODhkZGf369ZOuw2orqoG9AYBiktVXOy4uzsHBoXv37jExMd27d6cbO3To8Msvv8ydO5fL5dIt9K67Q4cO9FPF3J0CSAjVFABjuFyuo6MjTppqCBaLVa3FxMSEEPLmzRsx75LVmtfS0jpz5gwhZP78+e/fvxe1t2nTpnXr1g3sXGqtWrWSVVfYRAHgi3h7C3OgUwAAIABJREFUe/N4PH9/f2Vl5WqTvv/++9LSUvoxveuuugNXzN0pgCRQTUGTFB0dbW1t3bFjx0mTJtG/m588efLtt98aGhpyuVx3d3dNTU0zMzPxP6lFrl+/vmzZspUrV44cOfLEiROi9vDw8BUrVvj6+k6ePHnjxo0VFRWSzKjW3rKzsz08PPz9/T08PKZPn06f/x0REfHixYu8vDwPD489e/aID0lR1NGjR5cuXTpixAhra+t//vmHEPLs2bP169ebmJh8/PjRz8/PwMCgX79+sbGx5eXlPj4+PXv2NDAwEF0/I2bt1evXX3/19PRcu3att7d3VlaW+FTiV1FSUtKCBQt27drl4+OzbNkyMf0QQuLj4/X19W/cuCFJSFpCQgKbzTY1NaWfSr7m69oMCCH//vvv119/3bFjx6FDh6ampladZG9vv27duoKCAkdHRx6PV2ukWjekjIyMgICA/v37FxQUTJo0qVu3bnfu3PmiT7PWRaspNzf30KFD9+/fp59qaGi4ubn5+Pj4+PgYGxuzWKzg4GDJV1S13upauoZ8HwGgUdW6r6v61Rb//RWzW3769OmTJ080NDQmTJhQc2qvXr2qHTmvRpLdKYAiYvhMQ4AqiATnfNvY2HTq1GnhwoV//PFHYGCgqqqqrq5uSUlJVlYWvftevnz5s2fPHj9+3Lp169mzZ9c706CgoDlz5tAX3mzfvp0QEh0dTVHU3r17R48eTV9+k5eXZ2RkNHbsWKFQKH5GdfVmaWnp5OREv2bQoEHOzs7046lTp3bv3l2SlbNz585ffvmFoig+n9+3b18dHR0ul5uTk+Pi4kII8fT0TExMLC4uHjFihKGhoZeXV2pq6ufPn0eNGmVoaFjv2hM/6+DgYHNz87KyMnpVaGlpia6bqjWV+FVkYmISHx9PUVRpaemYMWPE9ENR1LVr11RUVIKDg+vKRp+J9/z585SUlKioKFdX1/bt2wcGBopeIOGar+uDc3Z2VlNT8/b2fv78eXJyspqa2pQpU0TvGjx4MEVRAoGAXl4fHx+6/ejRo4cOHaIf17Uh3bhxo3fv3kpKSlu2bDl+/LiZmdmTJ0++6NOsa9FSUlLI/183FR8fP2bMGEJIeHg4PVUU8vXr123atLGwsBAKhRKuqJq9Sfc1EU+S/QDguimoSZLvTq37umpfbfHfXzG75ZMnTxJChg0bVm/UXr16EUKq/vWRZHcqHq6bAqZgXwwKRMJqSldXV/R048aNhJCffvqJoqh169YRQvLy8uhJFhYWRkZG4nvLyclRV1d/8+aN6OmMGTNSU1Ozs7PV1NTOnj0reuXp06cJIUFBQWJmVFdvFEVZWlru2LGDbp83b97AgQPpxxJWUxkZGdra2qKxFjZv3kwIuXDhAkVRhw8fJoQkJyfTk7Zs2UIIefz4Mf1006ZNhJCcnJx6115duFxuly5dQkJCRC0zZsygqykxqepaRZWVlYSQgwcP0u10t2L6oSiKz+eLiUdXUy4uLtOnTx84cCCbzXZxcfn7779FL5BkzYv54JydndXV1UVjWnz11VddunQRdU7/+acoKjc3V19fnxBy+fJlqsqff/Eb0qJFiwghr169Ek39ok+zrkWrWk1RFBUZGSn6kSQUCtPT0+l2W1tbDofz9OlTyVdUtd6k+5rUC9WUJFBNQU31fnfE7OuqfrWp+r6/de2Wd+/eTQixtrauN2pd1RRV9+60XqimgCkcWR3jApCb9u3bix67ublt27YtMTGREKKkpEQIoccFIoTo6em9evVKfFfx8fFCobBHjx70Uy0trUuXLhFCrl69yuVy6R06berUqYSQ27dvu7i41DWjunojhMTGxhJCysrKgoODHzx4QFHUFy3yvXv3eDze4sWLRS3u7u4qKiqipRYNw0BXF6IT1g0MDAgh9AEluqWutVeXuLi4rKysAQMGiFpEl+XUm6rmKlJWVra2tl65cmVqauqOHTvmzJkjvh9RV+IFBQXRD/7+++8ZM2acP3/+8uXLdnZ2RLI1L+aDozOL1qehoeFff/1VswdNTc1Lly6NGTNm4cKFgwcPFrUnJCSI2ZCUlZU5HE7Pnj1FU7/o05Rwo1JVVRU9ZrFY3bp1I4RERERcv359zZo1/fv3pydJ0Zv4pZPi+wgAjUrMvq7qV5vU9/e0rt0yvY96+/ZtQ0LWtTsFUFiopqBp6969e6tWrcrKyqR7e0pKCo/Hoyiq2mAG7969I4QUFBSIWjQ1NVVVVTMzM6XojRAiEAh27dr1+PFjLy+vESNGJCQkfFHO58+fq6mpVbuYp1bVZk0/FQqFtb5YkrX34sULUuUHvXSpqrp8+bKHh8eRI0cuXbp08eLFsWPHStdPrYYOHbpr1y4nJ6fVq1fT1ZQka17MB1eNmBeYmpoeOHBg8eLFs2bNcnFxoX+FSLch1TW7ap+m1BsVl8tdtWqVgYEBfRhQ6t4auHQAIGeS7+ukQw8J+PbtWz6fL6rEpFDr7hRAYWEUCmja2Gw2h8MR/X/9S7Vv3768vLzauAIVFRX0v+5qXjRPjxf3pb0JhUJbW9unT5+GhYWNHTtWipyqqqofP378+PFj1cbc3FwpuqpKkrVHH4mifzfLJJWysnJISMjZs2cJIdbW1i9evJDt0g0bNowQ8urVKx6PJ+Gar+uD+9JZe3p6LliwIDEx8YcffqBbpNuQJNGQjcrf3//9+/cHDhxQU1NrSG+Nt3QA0Bhkta+ri4mJibGxMX0P8QZ2VXN3CqCwUE1B05aens7j8RwdHaV7+/DhwwkhmzZtEv2/PzEx8fz58+bm5u3atbty5YrolRkZGaWlpdOmTZOitwcPHkRGRlpZWdGN9L8G6cdsNrukpKTenAMGDKAoau3ataKW169fBwYGSr6ktZJk7Q0cOJAQcvHiRVGLUCgUCATSpaqoqDhy5AghxNnZOSEhQSgUxsTEiO+nrgNrtJonpL18+ZIQ0qtXL2VlZQnXfF0fnJj50rP+/PlztcYjR44MGTJENOyhdBuSJMQsmnipqal79+61s7Ozt7enWyIjI6XbRBtv6QCgMUi3r6uprt0yh8M5duwYIWT9+vX0VbJV/fvvv/QY6OT/d92i/Ywku1MAhYWDp9DEKCkpFRYWcrlcNTU1iqL8/f23bNnSu3dvQsinT58IIXw+n35ldnY2PQydmFMaRo8ePXny5IiIiAkTJjg4OLx79+7FixeXL1/mcDg7d+5csWJFdHQ0/RPzwIEDLi4u48ePFzOjunqjL0w6c+aMmZlZYmIiPcpFcnKytra2rq5uXl5eYmJiSUmJqalptTPXRSZOnGhqahoSElJeXj59+vTi4uLLly9fuHCBEFJcXFw1DJ1NdGCH/vsk+tejmLUnZhWNGzfu9OnTw4YNc3V1ffbsWXx8fG5ubkhIiL29fV2p6lpFhJCff/7Zy8uLvjGuurr6kCFDzM3N6+onKirKwcHh1KlTM2fOrJmNoih6RvQSEULS09NXrVpFCNm2bRv5/1PjJFnztX5w9JosKiqqrKykj9FlZ2dXVFSUlpaqqqp++PAhMzOzvLy8TZs2okht2rS5dOkSfXyMEKKpqSlmQyopKREIBEVFRRoaGvTrJf80xSyaaJ2IVj4hJC8vj366fPlyZWXlAwcO0E95PF5kZOSsWbMkXFFVexO/dFJ8HwGgUdX1R4rU2FGI+f6K3y1bWloGBgb6+vpaWlr+9NNP9M0qioqKbt68eeLECdE1rnT/RUVFbdu2JYRIsjsFUFzyGewCQBJEgrG8kpKSZs+ebWNj4+npuXLlStEARDExMfR5R8uWLcvJyTl37hz989rPz0/8oHBcLnfp0qVdu3bV1tZeunRpUVGRaFJERIS1tbWXl9fmzZv37NlDDyQtfkZ19bZkyZJ27dqZm5vfunXr2rVrmpqaM2fOLCkpSUpK0tPTMzY2vnjxovgFz8/PnzdvXufOnbW0tFxdXTMyMugw9LGjefPmvXr16s8//6Sv2bW1tU1OTr579+7QoUPpqa9fvxaz9sQrKipyc3PT1tY2MDDw8/Pz9PR0c3O7deuWQCCoK1Vdq4jL5Zqamk6ZMmXXrl2enp7Hjx8Xs3R0V126dLly5UrNVNHR0e7u7vR+rG/fvjY2NmZmZj179rSzs4uLixO9TMI1X+sHd/bsWbrO8fHx+fTp05kzZzp06EAI+eabb0JDQy0tLQkh06dPrzo72rVr1w4fPix+Qzpx4gQ9mMT8+fPpUfu+9NOsddFiYmJsbGwIIcOGDbt+/XpsbCyd08zMLCoqih4Irk+fPr6+vr6+vkuXLh00aNDy5cslXFHVehOzdFJ/HymM6ScZjOkHNUny3al1X1ftqy3++ytmtyzy6tUrT0/PESNG6Orqmpqajhs37siRI/RB79jYWNGdBm1tbUNDQyMiIiTfnYqBMf2AKSzqC8cWA2g8LBYrNDRU6tP2AKAZwH5AEmFhYfT9wZgOAgqkhX936GPsVc9LB5APnOkHzZ+enl5dV9kGBQVNnjxZznnqwmDOprKKAAAAABQKqilo/qoNFqewGMzZVFYRAAAAgELBmH4AAAAAAADSQDUFAAAAAAAgDVRTAAAAAAAA0kA1BQAAAAAAIA1UUwAAAAAAANJANQUAAAAAACANVFMAAAAAAADSQDUFAAAAAAAgDVRTAAAAAAAA0kA1BQAAAAAAIA1UUwAAAAAAANJANQUAAAAAACANVFMAAAAAAADSQDUFAAAAAAAgDVRTAAAAAAAA0uAwHQDgfzg5OTk5OTGdAgAAoOlp4X9DZ86cyXQEaIlQTYECCQ0NZToCND1ZWVlr1661tbWdPXs201lANkaNGsV0BICmR8K/oUVFRf7+/nw+f+vWrerq6o2dSp709fWZjgAtEaopUCCOjo5MR4AmSUVFZenSpcuXL7eysmI6CwAAMyT5G/rvv/9aWVmpqqrGxMR07dpVDqkAmj1cNwUATZ6np6ejo+P8+fPz8vKYzgIAoKA+fPgwZswYoVAYGxuLUgpAVlBNAUBzcOTIEWVl5fnz51MUxXQWAACF8/79+6+++kpZWTkmJkZXV5fpOADNB6opAGgONDQ0zp49GxkZefjwYaazAAAolvT0dEtLy3bt2t25c6dLly5MxwFoVlBNAUAzYWFhsXHjRl9f3ydPnjCdBQBAUaSlpY0ZM6ZDhw63bt3S1NRkOg5Ac4NqCgCaj02bNo0ePXrevHmlpaVMZwEAYF5aWpqlpaWenl5MTEynTp2YjgPQDKGaAoDmg81mnzlzJjs728fHh+ksAAAMe/PmjZWVVbdu3W7evNnMBkMHUByopgCgWdHT0ztx4sTx48cvXLjAdBYAAMZ8+PBhwoQJmpqa165da9++PdNxAJotVFMA0NxMnz59yZIlS5cuTU9PZzoLAAADsrOzJ06c2K5du1u3bnXs2JHpOADNGaopAGiG9u3bZ2Bg4OLiwufzmc4CACBXOTk548ePJ4TcvHkT10oBNDZUUwDQDLVp0yYkJCQxMXHbtm1MZwEAkJ+8vDwrKysejxcTE6Ojo8N0HIDmD9UUADRP/fr127Nnj7+/f0xMDNNZAADkoaioyMbGpri4OCoqCrfoBZAPVFMA0GwtW7Zs2rRp8+fPz8/PZzoLAEDjKi4utra2zsnJuX37drdu3ZiOA9BSoJoCgObs1KlTbDbbw8OD6SAAAI2otLR0ypQpmZmZsbGxPXr0YDoOQAuCagoAmrMOHTqcO3fu6tWrR48eZToLAECj4PF4jo6Oz58/j4yM7NmzJ9NxAFoWVFMA0MyNGTNm/fr1Pj4+ycnJTGcBAJAxiqIWL14cGxt79erVvn37Mh0HoMVBNQUAzd/mzZuHDRs2d+7csrIyprMAAMiSr6/vuXPnLl26NGrUKKazALREqKYAoPnjcDjnz5/Pysry9fVlOgsAgMxs27Zt//79Z8+etbGxYToLQAuFagoAWgR9ff3jx48fOXLkypUrTGcBAJCBY8eObdq0ae/evU5OTkxnAWi5UE0BQEvh4ODg7u6+cOHCd+/eMZ0FAKBBrly5snz5cn9//5UrVzKdBaBFQzUFAC3I/v37dXR0XFxcBAIB01kAAKQUExMze/bsxYsXb9y4keksAC0dqikAaEFUVVXDwsIePXq0Y8cOprMAAEjj0aNHX3/9tYODw8GDB5nOAgCopgCghenfv39AQMD3339/+/ZtprMAAHyZd+/e2dnZjRo16pdffmGz8SsOgHn4HgJAi7NixYqpU6e6uroWFBQwnQUAQFLFxcXTpk3r1KlTaGiosrIy03EAgBBUUwDQArFYrNOnT7NYLE9PT6azAABIhMfjzZw5Mzc39/r16+rq6kzHAYD/QDUFAC1Rhw4dgoKCrly5cuLECaazAADUz9vb++7du1euXDEwMGA6CwD8F6opAGihxo0bt2bNGm9v76dPnzKdBQBAnJ07d544cSIkJMTMzIzpLADwP1BNAUDLtXXr1iFDhsydO7esrIzpLAAAtbt48eLGjRv37dtnb2/PdBYAqA7VFAC0XBwO58KFCxkZGd999x3TWQAAavHw4cMFCxZ4e3uvWLGC6SwAUAtUUwDQohkYGBw7duzgwYO//vor01kAAP7H27dvp06d+tVXX+3Zs4fpLABQO1RTANDSzZo1a8GCBW5ubu/fv2c6CwDAfxQUFEyePNnAwCA0NFRJSYnpOABQO1RTAADk4MGD2trarq6uAoGA6SwAAEQgEMyZM6esrOy3335TU1NjOg4A1AnVFAAAUVNTCwsLu3///g8//MB0FgAA8t133925cyc8PFxHR4fpLAAgDofpAAAACmHAgAE7duxYs2aNpaXlqFGjRO03btzQ09MbMGAAg9kAcnJy9u7dK3r64sULQkjV0VO0tbV9fHwYSAaNICQkZM+ePadOnTI1NWU6CwDUg0VRFNMZAAAUAkVR9vb2T548SUpK6tChQ0VFxbp16/bv379p06bvv/+e6XTQogmFwq5du+bk5CgrK9ecWlFRsXLlyv3798s/GMhcUlLSqFGjli5dipEnAJoEVFMAAP+Vm5s7aNAgCwuLbdu2zZo1KzU1lc/n9+/fH3f4BcZ9++23Bw4cqKysrHXq/fv3cV/XZiA/P9/MzKxHjx5//PEHh4MTiACaAFRTAAD/Iyoqat68eZ8/fxYIBDwejxDCYrHev3+vp6fHdDRo0f7+++9hw4bVOklfX//du3csFkvOkUC2+Hy+tbX1mzdvHj16pKmpyXQcAJAIRqEAAPiv4uLi06dP5+bmVlRU0KUUIURJSenatWvMBgMYOnRor169arYrKysvWLAApVQzsHr16oSEhEuXLqGUAmhCUE0BAPzHw4cPBwwYcPHiRUJI1eP2QqEwIiKCuVwA/+Hs7Fzzuikej+fk5MRIHpChc+fOHTx48NSpU3UdgQQAxYQz/QAACCFk7969a9asIYTUesupVq1aFRYWqqqqyj0XwH+9evXKyMioWmPfvn2fPXvGSB6QlcePH1tYWHh7e+/cuZPpLADwZXBsCgCAEEImTpzYu3fvuk6X4vF40dHRco4EUE2vXr0GDhxYdStVVlaeP38+g5Gg4XJzc+3t7ceMGbNt2zamswDAF0M1BQBACCEDBgx4/Pjx+vXr2Wy2kpJStakcDufq1auMBAOoytXVter2yefzHR0dGcwDDURR1IIFC5SUlM6fP19zzwMAig9n+gEA/I+EhITZs2dnZmaKRqGgaWpq5uTk4Fp/YFZmZqa+vr5QKCSEsNlsMzOzv/76i+lQIL3du3evX7/+zz//rHrTcABoQnBsCgDgf5ibm6empi5evJgQwmb/dyeZl5eXmJjIXC4AQgjR1dUdNWoUvWWy2WxXV1emE4H0Hj58uHHjxh07dqCUAmi6UE0BAFSnqqp68ODBGzdudOzYUTSEWqtWrX7//XdmgwEQQlxcXOgHFEU5ODgwGwakVlRU5OTkNH78+NWrVzOdBQCkh2oKAKB2NjY2qampNjY2hBAWi1VZWXn58mWmQwGQmTNnstlsFotlZWXVuXNnpuOAlJYtW1ZaWnr69Omqx8ABoMnBFxgAoE5aWlpXr149efKkiooKi8VKSUnJyMhgOhS0dB07drS2tqYoSnSQCpqcwMDA0NDQ4OBgHR0dprMAQINgFAoAaER//fXX3r17mU4hA1wu98GDB/n5+UOHDjU0NGQ6TtND3xO58TSbLU1CHz58ePTokZ2dHYfDYTqL/DT2ViQ3KSkpZmZma9as8fPzYzoLADQUjk0BQCP68OFDeHg40ylkQE1NzdLScsCAAdnZ2UxnaWI+fvwoh22g2WxpEtLV1TUwMGg5pZR8tiL54HK5jo6OpqammzZtYjoLAMhAS9kRAwCDms1/lAkhaWlpxsbGTKdoSsLCwpycnOQzr+a0pdWroKCgY8eOTKeQE3luRY1t2bJl2dnZf/zxB+4uBdA8oJoCAPgCKKVAQbScUqo5CQoKOnv27O+//25gYMB0FgCQDZzpBwAAANDo0tPTV6xY4ePjY2try3QWAJAZVFMAAAAAjUsoFLq5uenp6W3fvp3pLAAgSzjTDwAAAKBx/fjjj3fv3r13716bNm2YzgIAsoRjUwAAAACNKDU1dfPmzVu3bh0+fDjTWQBAxlBNAQAAADSWysrKuXPnDh069Ntvv2U6CwDIHs70AwAAAGgs27Zte/XqVVJSEoZEB2iWUE0BAAAANIrk5OSAgIAff/yxZ8+eTGcBgEaBM/0AAAAAZI/P5y9cuHD48OHLli1jOgsANBYcmwIAAACQvd27d6ekpPz99984xw+gGcOxKQAAAAAZe/ny5datW7///vu+ffsynQUAGhGqKQAAAABZoijK3d29b9++q1evZjoLADQunOkHAAAAIEs///xzQkLCgwcPOBz80AJo5nBsCgAU0efPn5vlvACg2cvPz1+3bt2KFSuGDBnCdBYAaHSopgBAsRw7dmzcuHF9+vRpZvOSRFFR0caNG9etWyfh6y9fvmxpaclisVgs1qhRoywsLIYMGWJubr527drXr183atSmiM/nx8XFbdiw4ebNm3RLTEyMubl5enq6QqWSjtTLgq1I5lavXq2srLxlyxamgwCAPKCaAgDF4u7uLhQKBQJBM5tXvcLCwjw9Pbdv315SUiLhW2bMmHH27FlCSLdu3e7duxcfH//48eODBw8mJyebmJhs2LBBKBQ2ZuQm5uHDh6dPn96xY8fHjx/plsLCwg8fPnC5XIVKJZ2ay5KVlSXJG7EVyVZcXFxQUNChQ4fU1dWZzgIA8oBqCgAUi5KSkp6eXvObV70cHR1Pnjz5pe9q164dIURFRUXUYmpqeu3aNScnpx07dgQEBMgyYhM3cuTIFStWVG1xcHDIyMjo168fU5FIbamkU21ZioqK5s6dK+F7sRXJSmVl5eLFi21tbWfMmMF0FgCQE1RTAACKonXr1l/6FhaLVbORzWYHBgZ27tx5+/bt7969k0W0ZqJVq1ZMR6iFzFNxuVxHR0fJz/rDViQrBw4cSE9PP3ToENNBAEB+UE0BgEL49ddfPT09165d6+3tLTpD6cmTJ99++62hoSGXy3V3d9fU1DQzM3vz5o34SdLNixBCUdTRo0eXLl06YsQIa2vrf/75p94ZJSUlLViwYNeuXT4+PsuWLRPTj9Ti4+P19fVv3LjxRe9SV1d3dHQsLS0NDQ1V2EVjXG5u7qFDh+7fv0/qWxt1LXh2draHh4e/v7+Hh8f06dPz8/MJIRkZGQEBAf379y8oKJg0aVK3bt3odsmFh4evWLHC19d38uTJGzdurKioEE169OiRh4fH3LlzzczMjh07xufzay5LRETEixcv8vLyPDw89uzZQ7AVyUVOTs62bdvWrFnTvXt3prMAgBxRAACNRvQjTLzg4GBzc/OysjKKovLy8rS0tHR0dCiKysrKmjBhAiFk+fLlz549e/z4cevWrWfPni1+knTzoihq586dv/zyC0VRfD6/b9++Ojo6XC5X/IxMTEzi4+MpiiotLR0zZoyYfiRZXeXl5YQQLy+vqo3Xrl1TUVEJDg6u9S1FRUWEkN69e9ecdO7cOULIggULmF00CbeBBpJwLikpKYSQkydPUhQVHx8/ZswYQkh4eDhV3xZV14JbWlo6OTnRrxk0aJCzszNFUTdu3Ojdu7eSktKWLVuOHz9uZmaWkZEhYSqKovbu3Tt69OjKykqKovLy8oyMjMaOHSsUCimKSk9PV1NTe/v2LUVRrq6uhJBhw4atWrWq2rJQFDV16tTu3buLZoGtSA4WLlyop6dXUlLCdBAAkCtF3zcBQJMmyW8gLpfbpUuXkJAQUcuMGTNEFQ49wF1eXh791MLCwsjIqN5JUswrIyNDW1tbIBDQ7Zs3byaEXLhwQcyMKisrCSEHDx6k2+luxfRTr1qrKYqi+Hx+XW8R8zuYHiPOysqK2UVT2GqKoqjIyMiqFUhda0PMgltaWu7YsYNunzdv3sCBA+nHixYtIoS8evVKkvBVU2VnZ6upqZ09e1Y09fTp04SQoKAgiqJ8fX319fXp9rS0NELIsWPHal2WatUUha2okf39999sNvv8+fNMBwEAecNN5QCAYXFxcVlZWQMGDBC1VL2MRElJiRAiugOmnp7eq1ev6p0kxbzu3bvH4/EWL14smuTu7k5fl1/XjJSVla2trVeuXJmamrpjx445c+aI70dqdIAv9enTJ0KIsbGxIi8as1RVVas+rWttiFnw2NhYQkhZWVlwcPCDBw8oiqJfoKyszOFwevbs+aWREhISuFyuvr6+qGXq1KmEkNu3b7u4uGRkZJSWltLtRkZGmpqaHz58qHVZasJW1Hgoilq5cuWIESOcnJyYzgIA8oZqCgAY9uLFC0KIsrIys/N6/vy5mpraiRMnvqjDy5cve3h4HDly5NKlSxcvXhw7dqx0/TSG58+fE0IGDRrU/BZNzsQsuEAg2LVkGc2pAAAgAElEQVRr1+PHj728vEaMGJGQkNDAedHjPRQUFIhaNDU1VVVVMzMzCSG2trbnz5+Pjo62srIqKioqKSmxsbFp4BzFw1YkidDQ0Lt37z548KDW8TwAoHnDKBQAwDD66JB8Bg0TMy9VVdWPHz9Wu+dPbm6u+A6VlZVDQkLo2/VYW1u/ePFCun5kjqKo8PBwZWVlGxubZrZo8lfXgguFQltb26dPn4aFhY0dO1Ym8+rRowchpOZ4KiYmJoQQZ2fn48ePu7q6btq0afXq1efPnx89erRM5lsrbEWSqKys3LBhg4uLy7Bhw5jOAgAMQDUFAAwbOHAgIeTixYuilsa7o66YeQ0YMICiqLVr14omvX79OjAwUExvFRUVR44cIYQ4OzsnJCQIhcKYmBgp+qmXmNunik4tq+bHH398+vTp2rVru3XrpsiL1iTUteAPHjyIjIy0srKiG3k8Xl0fh+TMzc3btWt35coVUQt9dt+0adMIIeXl5WlpaUlJSf7+/j///PPXX39dVz9sNrvabaCxFTWSwMDAzMzMrVu3Mh0EAJiBM/0AgGGjR48eN27c6dOnhw0b5urq+uzZs/j4+Nzc3JCQkK+//pq+bEM0DHR2djY9HB+LxRIzSYp52dvbm5qahoSElJeXT58+vbi4+PLlyxcuXCD/f+lIzRkRQn7++WcvLy/6LsDq6upDhgwxNzevq5960ZfEVKsko6KiHBwcTp06NXPmzJpvobOJrqUhhLx79+7HH388dOjQypUrv//+e0LIxIkTGV80BUEvL5fLpZ9mZ2cTQvLy8qpOrbk26lqBr1+/JoScOXPGzMwsMTExNTU1Ozs7OTlZW1u7pKREIBAUFRVpaGh8USpNTc2dO3euWLGCPp2PEHLgwAEXF5fx48cTQg4dOhQdHd2zZ08dHZ22bdt26tSpf//+9Jmr1ZZFV1c3Ly8vMTGxpKTE1NT07t272Ioaw+fPn3fu3Ont7W1gYMB0FgBgiHwHvQCAlkXCkbiKiorc3Ny0tbUNDAz8/Pw8PT3d3Nxu3bp169Yt+sSnZcuW5eTknDt3Tk1NjRDi5+cXFRVV1yQxY5eJmZdAIMjPz583b17nzp21tLRcXV3pUa1jYmLqmhGXyzU1NZ0yZcquXbs8PT2PHz9Oz6LWfup148YN+jL9Hj16HD9+PDMzk26PiYnp0qXLlStXar4lMjKSHqKAEGJhYWFlZWVrazt58uRvvvkmKSmp6isZXDTFGdPv/v379FVGw4YNu379emxsrKWlJSHEzMwsKipKzNrg8/l1LfiSJUvatWtnbm5+69ata9euaWpqzpw5c//+/VpaWoSQ+fPnP378+ItS0Y0RERHW1tZeXl6bN2/es2cPPTw6RVExMTGdO3dms/97Xkn37t3fvn1bbVkoikpKStLT0zM2Nr548SKFrajRbNiwQUNDIz8/n+kgAMAYFtXg0xIAAOoSFhZG342H6SDAGPlsAy1kSzt79qy6uvrUqVPz8/Nzc3MzMjKSkpLy8vICAgKYjta4FPPzzcrKMjIy8vPz8/X1ZToLADAGZ/oBQHOjp6dXUVFR66SgoKDJkyfLOQ9NMVOBfMjk009MTPzuu+8yMjIIIVpaWlpaWn379h02bFhISIgss4LEtm3b1rFjRy8vL6aDAACTUE0BQHNTbbgwBaGYqUA+ZPLpp6SkZGZm+vv7T5kypU+fPp8/f05ISIiKitq1a1fDO4cv9eHDh59//vmnn35q06YN01kAgEkY0w8AAKAJcHZ23rRp0+HDh4cNG9a5c2c7O7uCgoKffvqpqd/6tonauXOntra2m5sb00EAgGE4NgUAANAEKCkpbd26devWraWlpSoqKrhRLIM+fPhw6tSpn376ib6FHQC0ZKimAAAAmhJVVVWmI7R0ODAFACKopgAAAAAkhQNTAFAVrpsCAAAAkNSePXtwYAoARFBNAQAAAEikoKDg1KlTPj4+ODAFADRUUwAAAAASCQwMVFZWdnd3ZzoIACgKVFMAAAAA9auoqDh8+PDSpUvbtm3LdBYAUBSopgAAAADqd+bMmcLCQi8vL6aDAIACQTUFAAAAUA+hULh3714XF5cuXbownQUAFAhGSAcAAACox82bN9PS0iIiIpgOAgCKBcemAAAAAOoRGBg4fvz4Pn36MB0EABQLjk0BAAAAiPP+/fsbN26EhoYyHQQAFA6OTQEAAACIc/To0c6dO0+bNo3pIACgcFBNAQAAANSpsrLy1KlTnp6eysrKTGcBAIWDM/0AoNHNmjWL6QjAmI8fP8ptXtjSFERxcXG7du1YLJasOpTnVlTTpUuX8vPzccdeAKgVjk0BQCPS19efOXMm0ykUxZMnT+7du8d0CnnT09OTwzaALU1xCIXCO3fuxMbGFhUVyapP+WxFdTl9+vSUKVP09PSYCgAAioxFURTTGQAAWgQ/P78LFy68ePGC6SAAjevly5fLli27ffu2u7v77t2727dvz3Qi6WVkZHTr1u3ixYvTp09nOgsAKCIcmwIAkBNjY+M3b97weDymgwA0LhMTk1u3bp04ceLy5ct9+vS5ePEi04mkd/bsWXV1dVtbW6aDAICCQjUFACAnJiYmPB7v7du3TAcBaHQsFmvhwoUvXryYPHmyk5OTvb19RkYG06GkERwcPGfOnNatWzMdBAAUFKopAAA5MTY2ZrFYaWlpTAcBkJNOnTqdPHnyzp07L1++7Nev308//SQUCpkO9QUePnyYkpLi6urKdBAAUFyopgAA5KRdu3Y6OjovX75kOgiAXFlYWDx+/HjJkiWrV6+2tLRsQl+Bs2fP9u7d28zMjOkgAKC4UE0BAMiPiYkJjk1BC6SiohIQEJCQkPDp06ehQ4ceOnRI8QfBEgqF4eHhc+fOZToIACg0VFMAAPJjYmLShP4xDyBbw4cPf/To0erVq318fCZNmsTsXaTq9ddff2VlZTk4ODAdBAAUGqopAAD5MTY2xrEpaMmUlZW3bt169+7dDx8+9O/f//jx40wnqlNERISRkVHfvn2ZDgIACg3VFACA/BgbG2dlZX369InpIABMMjMzS0xMdHV1XbJkiaOjY0FBAdOJahEREYFbQgNAvVBNAQDIj4mJCSHkn3/+YToIAMNUVVUPHDgQGRn5119/DRkyJD4+nulE/+Px48dv3rzBHXsBoF6opgAA5KdHjx6tWrXCpVMAtAkTJqSkpIwYMcLS0tLPz08gEDCd6D+uXLmip6c3fPhwpoMAgKJDNQUAID8cDsfQ0BCXTgGIqKurh4WFnTp1ateuXRMmTFCQm/zeuHFj6tSpLBaL6SAAoOhQTQEAyBUGogCoydXV9e7du1lZWYMHD46MjGQ2TGFh4d9//21tbc1sDABoElBNAQDIFQZJB6jVkCFDHj16NGnSJFtb2+3btzN4Q6qoqChCiKWlJVMBAKAJQTUFACBX9LEpxb91KYD8tW3b9ty5c4GBgVu3brWzsyssLGQkRlRUlJmZWYcOHRiZOwA0LaimAADkysTEhMvlKsjFIQAKyNPT8+7du0+fPjUzM0tOTpZ/gOjo6IkTJ8p/vgDQFKGaAgCQK2NjY0IILp0CEGP48OH379/X1dUdNWpUeHi4PGf9zz//vH37dsKECfKcKQA0XaimAADkSltbW0NDA5dOAYino6MTHR29YMECR0dHeV5GFRcXp6KiYm5uLp/ZAUBTx2E6AABAi4Nh/QAkweFwDh06NHDgQC8vr6dPn54+fVpFRaWxZ5qQkDB8+HBlZeXGnhEANA84NgUAIG/GxsY4NgUgIU9Pz2vXrt28eXP8+PHZ2dmNPbuEhAQcmAIAyaGaAgCQNxMTExybApDcxIkT7927l5uba25u/uLFi8ab0efPn1NTU0eMGNF4swCAZgbVFACAvBkbG6enp5eXlzMdBKDJ6NOnDz0uhYWFxf379xtpLg8fPhQIBKimAEByqKYAAOTNxMREIBC8efOG6SAATUmnTp2ioqLMzc2trKyuX7/eGLNISEjQ1dXV09NrjM4BoFlCNQUAIG/GxsZsNhsn+wF8KVVV1V9//XXu3Ln29vanTp2Sef9JSUmmpqYy7xYAmjGM6QcAIG8qKipdu3bFQBQAUlBSUjp27FjHjh3d3d0/ffrk4+Mjw86fPXtmb28vww4BoNlDNQUAwAAMRAEgNRaLFRAQ0Llz59WrV1dUVHz33Xcy6ZbH4/3zzz99+/aVSW8A0EKgmgIAYICJicmTJ0+YTgHQhH3zzTft2rVbsmRJRUXFli1bGt7hq1evKisr+/Xr1/CuAKDlQDUFAMAAY2PjsLAwplMANG0eHh4sFmvx4sVlZWUBAQEN7O3Zs2dsNtvExEQm2QCghUA1BQDAAGNj49zc3IKCgo4dOzKdBaAJc3d3Z7PZHh4eFEX98MMPDekqNTXV0NBQRUVFVtkAoCVANQUAwAD6/99paWnm5uZMZwFo2hYuXKikpLRo0SIVFRU/Pz+p+3n16hUOTAHAl0I1BQDAgG7durVp0+bly5eopgAabv78+Xw+38PDo2PHjt7e3tJ18uHDh969e8s2GAA0e6imAAAYwGaze/XqhWH9AGRl0aJFXC531apVbdu2XbhwoRQ9ZGRkWFlZyTwYADRvqKYAAJhhbGyMagpAhry9vbOysjw9PdXV1R0cHL707RkZGXp6eo0RDACaMVRTAADMMDEx+f333wkhFEW9f/8+LS2Ny+V+/fXXTOcCaMJ27NhRUFDg7Oysqak5btw4yd9YUFBQWlqKagoAvhSqKQAAuSoqKkpLS3v58mVaWlpeXl6fPn3evHlTWVlJCHF2dkY1BdAQLBYrMDCwsLBw+vTpf/31l+SjSnz8+JEQ0rVr18ZMBwDNEKopAAA5KSgoGDBgQGZmJiGEzWYrKyvzeLysrCx6KofDGTx4MKMBAZoDJSWloKCgr776ytbW9v79+5qampK8Kzs7mxDSpUuXRk4HAM0Nm+kAAAAtRceOHadPn85mswkhQqGwoqJCKBSKpvL5fFRTADLRpk2biIgIPp8/Y8YM+sBvvYqLi1ksVrt27Ro7GwA0M6imAADkZ/Pmza1bt65r6qBBg+QZBqAZ09HRuXHjRnJy8pIlSyR5/efPn1VUVJSUlBo7GAA0M6imAADkp3Pnzj4+PhxOLWdZ6+joSHhKEgBIom/fvmfPng0KCtq7d2+9L/78+TMOTAGAFFBNAQDI1Zo1a1RVVas1slgsU1NTRvIANGN2dnY7d+5cu3ZtXFyc+FeWlJS0bdtWPqkAoDlBNQUAIFfq6urr16+vdkKRsrLy0KFDmYoE0Iz5+vra2dk5Ojr++++/Yl6GY1MAIB1UUwAA8ubt7a2pqclisUQtPB4PQ1AANAYWi3Xq1CkVFZV58+YJBIK6XlZaWqqmpibPYADQPKCaAgCQNxUVFT8/v6rVFEVRQ4YMYTASQDOmoaERGhp69+7dnTt3inkZRVFyiwQAzQaqKQAABixatEhfX58eLZ0Q0rZtWwMDA2YjATRjpqamu3fv9vPzu337dq0vUFJSEnPkCgCgLqimAAAYoKysvGPHDtH/wgcPHlz1UBUAyNyKFSumTZu2YMGC4uLimlM5HA6fz5d/KgBo6lBNAQAwY86cOf369VNSUmrVqpWZmRnTcQCav+PHj5eXl3/77bc1J+HYFABIB9UUAAAzWCxWQECAQCDg8Xi4by+AHGhqah47duzEiRN//PFHtUmopgBAOrXcQRIAICwsjOkILYWRkdE///yTk5ODdc4sR0dHpiOAPNjb28+cOdPd3T0lJUVDQ0PUjjP9AEA6LIxgAwA14RoeaGnw17DlyMvL69+/v52d3YkTJ0SN+/fv37VrV2ZmJoPBAKApwpl+AFC70NBQCuRi8+bNTEdo0UJDQ5n+toFcaWpqHjx48Oeff46Li6vamJeXR6GoBoAvhGoKAIBhmzZtYjoCQMsya9asSZMmrVy5UnStlKamJo/H+/z5M7PBAKDJQTUFAMAwDgeXsALI2969e1NSUk6dOkU/7dSpEyEkLy+P0VAA0PSgmgIAAIAWp0+fPsuWLduwYUNRUREhRFNTkxCSn5/PdC4AaGJQTQEAAEBL5OfnRwjZunUrwbEpAJAWqikAAABoiTQ0NPz9/Q8dOpSWlta+fXsNDY13794xHQoAmhhUUwAAANBCubu79+zZc/v27YSQXr16/fPPP0wnAoAmBtUUAAAAtFBKSkobNmwIDg5OS0szNjZOS0tjOhEANDGopgAAAKDlmjNnTq9evQICAoyMjHBsCgC+FKopAAAAaLmUlJTWrVsXFBTUrl27t2/f8vl8phMBQFOCagoAAABaNGdnZ0NDw7i4uMrKSgxEAQBfBNUUAAAAtGhKSkrffffd9evXlZSUnjx5wnQcAGhKUE0BAABASzdv3jwNDQ1NTc379+8znQUAmhJUUwAAANDStW7dev78+SUlJQkJCUxnAYCmBNUUAMjM58+fm2jniqakpITpCAAtzpIlS0pLSx88eICBKABAcqimAEAGjh07Nm7cuD59+jRG54cPHx4zZoy5ubnMey4qKtq4ceO6deskfH1kZKSrqyuLxWKxWCNHjrSysho5cuSIESP27dsnq/onODh4woQJRkZGNSdduHChX79+LBZrzJgxVX/tFRYW+vv7t2/fXk1NbdOmTfn5+TJJIjk+nx8XF7dhw4abN2/SLTExMebm5unp6TLpX7a9AdSlZ8+e5ubmFRUVKSkpTGcBgCYD1RQAyIC7u7tQKBQIBI3R+eLFiz99+iQUCmXbbVhYmKen5/bt2yUvhKytrX/55Ze2bdsSQu7evRsdHf3XX395e3v7+vra2dnxeLx6e8jKyhL/gtmzZwsEglr/NT579uzbt29zOJz4+PgNGzaI2jt06LBp06aFCxe6ubn5+/t36tRJwsWRlYcPH54+fXrHjh0fP36kWwoLCz98+MDlcqXus+qKanhvABJavXo1IeTatWtMBwGAJgPVFADIgJKSkp6eXiN1zuFwunbtKvNuHR0dT548+aXvYrPZ6urq9AO6Zd68eQ4ODrdv375796749xYVFc2dO1f8a8SvSS0tLW1tbXV19d27d//+++9VJ/Xo0cPQ0FCiZZC1kSNHrlixomqLg4NDRkZGv379pOuw2opqYG8AkrO3t2/dunVoaCjTQQCgyUA1BQAtV+vWraV4F4vFqtZiYmJCCHnz5o2Yd3G5XEdHx4afrqalpXXmzBlCyPz589+/fy9qb9OmjXSLIxOtWrWSVVeyWlEAUuBwOMOHD09NTcWlUwAgIVRTACC9X3/91dPTc+3atd7e3qJTs548efLtt98aGhpyuVx3d3dNTU0zMzO60hAzSRLR0dHW1tYdO3acNGmSTDqsVXx8vL6+/o0bNyR/S0JCApvNNjU1pZ9mZ2d7eHj4+/t7eHhMnz6dvpApIiLixYsXeXl5Hh4ee/bsoV95/fr1ZcuWrVy5cuTIkSdOnKja57///vv111937Nhx6NChqampVSfZ29uvW7euoKDA0dGxrtMLw8PDV6xY4evrO3ny5I0bN1ZUVBBCMjIyAgIC+vfvX1BQMGnSpG7dut25c2f9+vUmJiYfP3708/MzMDDo169fbGxseXm5j49Pz549DQwMRFdD1bVoNeXm5h46dEg00rSGhoabm5uPj4+Pj4+xsTGLxQoODpZ8RVXrra6la4yNAVogT09PgUAQHh7OdBAAaCIoAIAaCCGhoaHiXxMcHGxubl5WVkZRVF5enpaWlo6ODkVRWVlZEyZMIIQsX7782bNnjx8/bt269ezZs8VPEs/GxqZTp04LFy78448/AgMDVVVVdXV1S0pKpO6QVl5eTgjx8vKq2njt2jUVFZXg4OC63kWfiff8+fOUlJSoqChXV9f27dsHBgaKXmBpaenk5EQ/HjRokLOzM/146tSp3bt3F70sKChozpw5AoGAoqjt27cTQqKjoymKcnZ2VlNT8/b2fv78eXJyspqa2pQpU0TvGjx4MEVRAoGAXnAfHx+6/ejRo4cOHaIf7927d/To0ZWVlRRF5eXlGRkZjR07VigU3rhxo3fv3kpKSlu2bDl+/LiZmdmTJ09cXFwIIZ6enomJicXFxSNGjDA0NPTy8kpNTf38+fOoUaMMDQ3rXTT6qv2TJ09SFBUfHz9mzBhCSHh4OD1VFPL169dt2rSxsLAQCoUSrqiavdW1dFJvDPRpXfW+DFoIHo+npKT01VdfMR0EAJoG/P0AgFrUW01xudwuXbqEhISIWmbMmEFXUxRF0aPk5eXl0U8tLCyMjIzqnSSGjY2Nrq6u6OnGjRsJIT/99JPUHdJqraYoiuLz+WLeRVdTLi4u06dPHzhwIJvNdnFx+fvvv0UvsLS03LFjB/143rx5AwcOpB9XLRJycnLU1dXfvHkjejpjxozU1FSKopydndXV1elqgaKor776qkuXLqLO6WqKoqjc3Fx9fX1CyOXLl6kq1VR2draamtrZs2dFbzl9+jQhJCgoiKKoRYsWEUJevXolmnr48GFCSHJyMv10y5YthJDHjx/TTzdt2kQIycnJEb9oVaspiqIiIyNF9Y9QKExPT6fbbW1tORzO06dPJV9R1XoTv3TSbQyopqCafv36qaqqMp0CAJoGnOkHANKIi4vLysoaMGCAqKXqlTNKSkqEEA6HQz/V09MT3S1KzCTx2rdvL3rs5uZGCElMTGxIh2LQfYoXFBR0+fLlpKSkhw8f3rlzx8zM7LfffqMnxcbGrlu3rqys7OTJkw8ePCgtLa359vj4eKFQ2KNHD/qplpbWpUuXREPMKysrKysr048NDQ0LCwtr9qCpqXnp0qXWrVsvXLjw7du3ovaEhAQul0sXWrSpU6cSQm7fvk33zOFwevbsWW1hRYNq0LWiaO4GBgaEkLy8PMkXjRCiqqoqesxisbp160YIiYiIuH79+jfffNO/f3+pexO/dI2xMUALNHv27NLSUtzGFwAkgWoKAKTx4sULUuU3t5x17969VatWZWVljMy9mqFDh+7atYvP59NjKxNCBALBzp0758+fb2xsPGLEiFrflZKSwuPxKIqqt/+ag16ImJqaHjhwoKioaNasWfRxNkLIu3fvCCEFBQWil2lqaqqqqmZmZkqyONVmRz8VDU8vyaLVisvlrlq1ysDAYPPmzaJGKXpr4NIBSMLb27tPnz5PnjxhOggANAGopgBAGvSRKPqnrfyx2WwOhyM6xMG4YcOGEUJevXrF4/GEQqGtre3Tp0/DwsLGjh1b11vat29fXl5ebXgJejSFL+Lp6blgwYLExMQffviBbqGPd9UcfYEeeLAhJFy0Wvn7+79///7AgQNqamoN6a3xlg5ApH379j169IiNjWU6CAA0AaimAEAaAwcOJIRcvHhR1NJ4d++tKT09ncfjOTo6NlL/4u8UXPOA0suXLwkhvXr1UlZWfvDgQWRkpJWVFT2p6gEoNpstulPw8OHDCSGbNm0SzSsxMfH8+fPig1EUVfPUtSNHjgwZMkQ0pqK5uXm7du2uXLkiekFGRkZpaem0adPEd14vMYsmXmpq6t69e+3s7Ozt7emWyMhICVdUNY23dABVWVlZRUdHy/ym4QDQ/KCaAgBpjB49ety4cadPnz569GhpaenDhw/j4+Nzc3NDQkJKS0s/ffpECBHdsCU7O5se+o8QImaSGEpKSoWFhVwulxBCUZS/v/+WLVt69+4tdYc0+kKdakVgVFSUhoZGXeMjUxRFz5EOQwhJT09ftWoVIWTbtm3k/0+NO3PmzNOnT3/55ZfU1NTs7Ozk5OTs7GxdXd28vLzExMQ///xzyJAhkydPjoiImDBhwuHDh9esWfP99987OzsTQnJzc4uKiiorK0VLVFFRQUf98OFDZmam6KQ+Wps2bS5dutShQwf6qaam5s6dO+/evRsdHU23HDhwwMXFZfz48YSQkpISgUBQVFQkentxcXHVFUgvXW5uLv2ULt7og2ZiFq3aOsnOziZVrrZavny5srLygQMH6Kc8Hi8yMlLCFVVaWlq1N/FL15CNAaCqCRMm5Ofn42Q/AKgfM4NfAIBiIxKMkF5UVOTm5qatrW1gYODn5+fp6enm5nbr1q1bt27Rp2MtW7YsJyfn3Llz9Mldfn5+UVFRdU0SP4xeUlLS7NmzbWxsPD09V65cKRosOyYmRroOKYq6cePGnDlzCCE9evQ4fvx4ZmamqM8uXbpcuXKl5luio6Pd3d3pnWffvn1tbGzMzMx69uxpZ2cXFxcnetmSJUvatWtnbm5+69ata9euaWpqzpw5s6SkJCkpSU9Pz9jY+OLFixRFcbncpUuXdu3aVVtbe+nSpUVFRRRFnT17VkNDgxDi4+Pz6dOnM2fO0GXSN998ExoaamlpSQiZPn161dnRrl27dvjwYdHTiIgIa2trLy+vzZs379mzhx6R/MSJE1paWoSQ+fPn06P2xcTE0IcZ582b9+rVqz///HPw4MGEEFtb2+Tk5Lt37w4dOpSe+vr167oWLSYmxsbGhhAybNiw69evx8bG0jnNzMyioqLoEfP69Onj6+vr6+u7dOnSQYMGLV++XMIVVa03MUsn9caAMf2gJqFQqKOj88MPPzAdBAAUHYvCP+0AoAYWixUaGtp4p9IBKI6wsDD6tldMBwHFMm/evLy8vKp3rwYAqInDdAAAAEII0dPTq2sMhqCgoMmTJzPeIQC0KFZWVsuXLy8rK1NRUWE6CwAoLlRTAKAQPn78qOAdAkCLMnHixPLy8nv37onGSgEAqAmjUAAAAABUp6+v36tXL/rG0AAAdUE1BQAAAFCLsWPHxsXFMZ0CABQaqikAAACAWowdOzYhIaHaPQkAAKpCNQUAAABQi7Fjx1ZUVDx8+JDpIACguFBNAQAAANSiR48eBgYGd+7cYToIACguVFMAAAAAtfs/9u48Lqqy///4NcwAAoKmIKC475Yamgju3ZqpmUV0LrkAACAASURBVLtouZfS7a6l+fNOS79mdGtlau6WiUKhuJVLqbhiLt3mvtTtLogsKggDCsOc3x+ne0SWYRzFc2Z4Pf+ac51zrvM554Eyb65zrtO6dWvSFAAzSFMAAAAFa9OmzW+//Zadna10IQBUijQFAABQsDZt2qSnp586dUrpQgCoFGkKAACgYHXr1i1XrtyRI0eULgSASpGmAAAACqbRaAICAo4ePap0IQBUijQFAABQqObNmzM2BaAwpCkAAIBCNW/e/PLly0lJSUoXAkCNSFMAAACFCgoKEkJwsx+AApGmAAAAClW2bNk6deqQpgAUiDQFAABgTkBAwLFjx5SuAoAa6ZQuAIBKHT58WOkSgOeBH3UUqUmTJrNnz1a6CgBqpJEkSekaAKiORqNRugTgueK3IczYv39/u3btbt686efnp3QtANSFsSkABeCbpQ05evRo69at58yZM2HCBKVrAeyTv7+/RqP5448/SFMA8uC5KQCwbc2bN585c+aHH37IK3GAYuLh4VGzZs0TJ04oXQgA1eFOPwCweUajsXPnzpcuXfrjjz/KlCmjdDmAHQoODs7Kytq8ebPShQBQF8amAMDmOTg4rFmzJjMzc8SIEUrXAtgnf39/xqYA5EeaAgB7UKFChYiIiI0bNy5fvlzpWgA71Lhx4xs3bqSkpChdCAB1IU0BgJ1o167d1KlTx48ff+rUKaVrAexNgwYNhBDnz59XuhAA6kKaAgD7MXPmzBYtWgQHB6elpSldC2BXqlat6u7uTpoCkAdpCgDsh4ODw+rVq+/evcts6cCzpdFo6tWrd+7cOaULAaAupCkAsCt+fn6rV69etWrVmjVrlK4FsCsNGjRgbApAHqQpALA3Xbp0mTBhwqhRoy5evKh0LYD9aNCgAWNTAPLgfVMAYIeys7PbtGmj1+uPHj3q4uKidDmAPdi2bVvXrl1TUlJ4qxsAE8amAMAOOTo6hoeH37hx48MPP1S6FsBO1KpVSwhx+fJlpQsBoCKkKQCwTzVq1Fi5cuWiRYs2btyodC2APahevbpWq7106ZLShQBQEdIUANitPn36hISEvPvuu1evXlW6FsDmOTk5ValShbEpALmRpgDAnn399dfVqlXr379/VlaW0rUANq9mzZqkKQC5kaYAwJ6VKlVq3bp1Fy5c+Pjjj5WuBbB5tWrV4k4/ALmRpgDAztWuXXv+/Plz5sz5+eefla4FsG21atVibApAbqQpALB/w4YNGzhw4Lvvvnvr1i2lawFsWNWqVW/dusV9swBMSFMAUCIsWbKkfPnyb7/9dk5OjtK1ALaqcuXKRqMxPj5e6UIAqAVpCgBKBDc3t3Xr1h07dmz27NlK1wLYKj8/PyHEzZs3lS4EgFqQpgCgpGjYsOHcuXNnzpwZHR2tdC2ATfLx8XF0dIyNjVW6EABqQZoCgBJk9OjRwcHBAwYMSEhIULoWwPZotVpfX1/GpgCYkKYAoGRZsmSJq6vrsGHDJElSuhbA9vj5+TE2BcCENAUAJUvZsmUjIyOjo6O//PJLpWsBbI+vr+/t27eVrgKAWpCmAKDEadas2axZs6ZOnXr48GGlawFsTIUKFRITE5WuAoBakKYAoCSaPHly586d+/fvf/fuXaVrAWwJaQpAbqQpACiJNBrNt99+azAYQkJClK4FsCVeXl5JSUlKVwFALUhTAFBCeXl5RUREbN68ecmSJUrXAtiMChUq3Llzx2AwKF0IAFUgTQFAydW2bdtp06ZNnDjxxIkTStcC2IYKFSoYjUZukQUgI00BQIn28ccft2rVql+/fmlpaUrXAtiAsmXLCiFSUlKULgSAKpCmAKBEc3BwCA8PT0tL4wEqwBIeHh5CiPv37ytdCABVIE0BQEnn7e29atWqyMjI1atXK10LoHZymkpNTVW6EACqQJoCAIhOnTp98MEHo0ePvnDhgtK1AKpWpkwZQZoC8D+kKQCAEEKEhoY2btw4ODg4MzNT6VoA9dLpdK6urtzpB0BGmgIACCGETqf78ccfb9269cEHH+RuDwsL+/zzz5WqClAhDw8P0hQAGWkKAPC3ypUrL1++fMmSJT/88IMQ4t69e927dx8yZEhYWJjSpQEq4uzs/ODBA6WrAKAKOqULAACoSO/evUeNGjVy5EhnZ+exY8cmJSUJIS5cuHD16tXq1asrXR2gCqVKlSJNAZAxNgUAeMwXX3xRunTpvn37JiQkZGdnCyF0Ot1PP/2kdF2AWpQqVerhw4dKVwFAFUhTAIBHkpOTe/bsGR8fbzQac3Jy5MacnJwNGzYoWxigHqQpACakKQDA3/bt2/fiiy/u2bPHaDTmbpck6dChQ8nJyUoVBqgKd/oBMCFNAQCEEGLTpk3t27dPSkqS7+7Lb8eOHc+5JECdHBwc8vzFAUCJRZoCAAghRI8ePebPn+/k5OTo6Jh/rUaj2bhx4/OvClAhSZI0Go3SVQBQBdIUAEAIITQazZgxYy5cuBAQEODgkPe3Q05Ozq+//srdTYAQwmg05v83AqBk4v8CAMAj1atXP3DgwFdffeXo6KjTPfYWjczMzD179ihVGKAepCkAJvxfAAB4jIODw/jx40+ePPnSSy9ptVpTu6Oj45YtWxQsDFAJ0hQAE/4vAAAUoEGDBr///vvs2bN1Op08SJWdnR0VFcXD9wBpCoAJ/xcAAAqm0+mmTJly+PDhGjVqyIHq7t27x44dU7ouQGGkKQAm/F8AADDnlVdeOXXq1AcffCB/feRmP4A0BcBEI0mS0jUAgFr07ds3KipK6SoAi/Tp02f9+vVKV1ESNW3atGPHjqGhoUoXAkB5uqI3AYCSJDAwcOLEiUpXoVJZWVlRUVGvvvqqr6+v0rWUdPPmzVO6hJKLsSkAJqQpAHiMn59fcHCw0lWo18CBAw0GQ57J0/H8MSqlIKPRyNt7Acj4ywoA4MkQpVDC6fV6Nzc3pasAoAqkKQAAgCdw//59Dw8PpasAoAqkKQAAgCdAmgJgQpoCAACwVFZW1sOHD93d3ZUuBIAqkKYAAAAsdf/+fSEEY1MAZKQpAAAAS5GmAORGmgIAALAUaQpAbqQpAAAAS6WlpQnSFID/IU0BAABYirEpALmRpgAAACx1//59rVbr6uqqdCEAVIE0BQAAYKmUlJSyZcsqXQUAtSBNAQAAWOr27ds+Pj5KVwFALUhTAAAAlkpISPD29la6CgBqQZoCgGdAnuYLgN27ffs2aQqACWkKAJ7KsmXL2rZtW79+faULKdiePXsCAwOvXbumqq7MSElJmTZt2tSpU63u4Y033khKSrJuX5u7XHj+GJsCkBtpCgCeyvDhw41GY05OjtKFPBIfH2/6fO/evZs3b+r1esW7ssS6detCQkJmz56dnp5uXQ9//vnn9u3bV6xYYfkutnu5oAjSFIDcSFMA8FS0Wq2fn5/SVTySkpLy9ttvmxZ79+4dFxf34osvKtuVhYKDg1euXPk0PSxdurRUqVJLliwxGAyWbG/TlwuKSExMJE0BMCFNAYD90Ov1wcHBz+TusmfY1RNxdna2et/MzMzDhw9PmjQpNjZ28+bNRW5vB5cLz1l6erperydNATAhTQGANbZs2RISEjJlypRx48aZ7u86efLk5MmTa9Soodfrhw8f7unpGRAQcOXKFfOrzEtISBgxYsSsWbNGjBjRs2fPO3fumFZt37591KhR48ePDwoKku9t27Rp08WLF5OTk0eMGPHFF18IIZKSkr755pujR48eOHDAy8tLo9FMmzZN3j06OtrDw+OTTz4p8BBmujIVEBUVNXbs2EmTJnXu3HnatGkPHz58mjM1IyYmpnLlyjt27DC/2Q8//BAcHPzee+9ptdqFCxfmWVtyLheKT0JCghCCNAXgEQkA8D99+vTp06dPkZuFh4cHBgZmZmZKkpScnOzl5eXj4yNJUnx8fIcOHYQQo0ePPnfu3IkTJ5ydnfv3729+lXnt2rXr16+f/Llx48YDBw6UP4eFhb311ls5OTmSJM2ePVsIER0dLUlS165dq1WrJm8TExPTunVrIURUVJQkSfK3/I0bN8prs7OzW7dubTQaCzuEma4kSfrqq69atmyZlZUlX4TatWu3adPGaDRafaayBw8eCCHGjBmTu3Hbtm0uLi7h4eHm923VqlVycrIkST169BBCnDx50rTK/i6XhT+reLZiYmKEEDdv3lS6EABqwdgUADyZjIyMSZMmjRs3rlSpUkKI8uXLy9+bhRA+Pj7NmjUTQsycObNBgwYvv/xys2bNjh8/bn5VkRo3bix/eOmll06fPi2ESEpKGjt27OzZsx0cHIQQI0aM6NWrl6+vb54dW7ZsOX36dNPiP//5z3LlyoWHh8uLv/76a//+/TUaTYGHMN9VYmLi9OnT//nPfzo6OsoX4V//+teBAwfWrl37NGdamC5duqSlpeV+Kim///znP9WrVy9fvrwQYuTIkUKIb775Rl5V0i4Xik9CQoJGo/Hy8lK6EABqQZoCgCdz8ODB+Pj4hg0bmlqcnJxMn7VarRBCp9PJi35+fqZXUZlZZcbevXunTp2amZm5cuXKY8eOZWRkCCFiYmKMRmP16tXlbby8vDZs2FDgLO2urq6mz25uboMHD/7pp5+Sk5OFEJGRkW+99VZhhzDf1ZEjR/R6feXKlU0tXbt2FULs27fP6jM1T+7TjMWLF7/33nvy59dee61WrVrh4eF3794VJfJyoZhcv37d29v7aZ7uA2BnSFMA8GQuXrwohJAHGZ6DnJyc0NDQIUOG1KlTp3nz5nLj2bNns7OzJUl60t5CQkKys7PXrl2bkpKi1WpfeOGFwg5h3vXr14UQclaReXp6urq63rp160lLeiZSUlJ27do1adKkoKCgoKCgFi1aCCEyMzO//fZbweXCs3Pz5s0qVaooXQUAFSFNAcCTkUei5O/Hxc1oNHbp0uXMmTPr1q1r06aNqd3Dw+PBgwfnz5/PvbE8q4F59evXb9269XfffRcZGTlgwAAzhzBPHufJP1lC3bp1Lezh2fr2228nT558OJc9e/ZotdrFixcbjUYuF56VGzdukKYA5EaaAoAn06hRIyHE+vXrTS3F9/beY8eO7dy5s3379vKiaYDllVdeEUJMnz7daDTKq44fP/7DDz8IIRwcHMy/+jYkJOTMmTNhYWH/+Mc/zBzCfFeBgYHu7u65ZyGPi4vLyMjo1q3bU5yuOaYzzS8rK+ubb77J81RV5cqVO3XqdO3atZ9//rkEXi4UE9IUgDxIUwDwZFq2bNm2bdtVq1YtXbo0IyPj999/j4mJSUpKioiIyMjISE1NFUKYXh2bkJAgT/0nhDCzqjDynAerV68+c+bM999/f/78+YSEhNOnT9eqVatz586bNm3q0KHDokWLPvzww5kzZw4cOFAIUbFixeTk5OPHj+/fvz8jI0Oe0Fl+8kfWp0+fF1544bXXXpOnZCjsEAkJCWa68vT0DA0NPXToUHR0tNztggULBg0aJEcOK87URH4MKU863bVrV9myZaOiogrcZfXq1b6+vp6ennna5WeT/u///q9Fixb2ernwnJGmAOShnTFjhtI1AIBayCNOwcHB5jfr2bPn7du3V6xYsXTp0tKlS/v6+jZq1CgoKCg2NvbLL79MSUlJT08PCAjYvHnzsmXL0tLSNBqNwWAobFXr1q3lb+r5+fn5JSQk7Nq16+jRoz179nz11Ve3bt1648aN4ODgfv363bt377ffftu3b1/NmjUXLVokT3tQpUqVrVu3/vTTT4GBgffu3ZszZ861a9cSExOrV69eo0YNIYROp9Pr9SNHjixdurT5Q9SsWdNMVwEBAY0bN54/f/6xY8eOHj1arly5OXPmaDSavXv3fvHFF096prJffvklNDT07Nmzd+7ccXV1rVixoru7uxDixo0bW7du7datW7169fLssmnTpgkTJsTHx7u7uwcEBJjaDx8+vGrVqr/++is+Pv7y5csLFy7MzMy0p8tl4c8qnqGHDx/+61//CgkJKXAKEwAlk4a/fgGASd++fcXjd/EB6sTP6vN36dKl2rVr/+c//2natKnStQBQC53SBQBASefn51fYjAhhYWGdO3d+zvUUn5JzprBLN27cEEJwpx+A3EhTAKCw2NhYpUt4TkrOmcIu3bhxw8XFJf8TegBKMmahAAAAKNrNmzcrV64sz0QCADLSFAAAQNGuXr0qvzoMAExIUwAAAEUjTQHIjzQFAABQNNIUgPxIUwAAAEXIzs6OjY0lTQHIgzQFAABQhBs3buTk5JCmAORBmgIAACjC1atXhRA1atRQuhAA6kKaAgAAKMKVK1fc3d3LlSundCEA1IU0BQAAUISrV68yMAUgP9IUAABAEUhTAApEmgIAACgC06MDKBBpCgAAoAhXr16tVq2a0lUAUB3SFAAAgDl6vT4pKYmxKQD5kaYAAADMkadHJ00ByI80BQAAYM61a9eEEFWrVlW6EACqo1O6AABQl6ioKI1Go3QVQNH69OmjdAklxdWrV728vEqXLq10IQBUhzQFAI+8//77ffv2VbqKEuSHH344cODAokWLHBy4V+KJVa5cWekSSopr165xmx+AApGmAOCRoKCgoKAgpasoQV5++eW6det6eXm1b99e6VqAQjGhH4DC8LdAAIBi6tSpExAQEBYWpnQhgDmMTQEoDGkKAKCkwYMHb9iwIS0tTelCgEIxNgWgMKQpAICS3nrrLYPBsHHjRqULAQqWmpqakpJCmgJQINIUAEBJ5cqVe+ONN7jZD6rFy6YAmEGaAgAobPDgwXv37pW/swJqc/XqVY1GU6VKFaULAaBGpCkAgMLeeOONChUqREREKF0IUIBr1675+Pi4uLgoXQgANSJNAQAUptPp+vXrt3r1akmSlK4FyIsJ/QCYQZoCAChv8ODB//3vf48ePap0IUBeTOgHwAzSFABAeU2bNm3YsOHq1auVLgTI6+rVq4xNASgMaQoAoAqDBg364YcfMjMzlS4EeMz169cZmwJQGNIUAEAVBgwYkJ6evm3bNqULAR5JTk5OS0sjTQEoDGkKAKAKFStW7NChAzf7QVV42RQA80hTAAC1GDJkyC+//HL79m2lCwH+du3aNQcHh8qVKytdCACVIk0BANSiZ8+epUuX/vHHH5UuBPjb1atXK1Wq5OTkpHQhAFSKNAUAUItSpUr17t2bm/2gHrxsCoB5pCkAgIoMGTLk5MmTp0+fVroQQAghrl27VrVqVaWrAKBepCkAgIq0atWqZs2aYWFhShcCCCHEzZs3q1SponQVANSLNAUAUBGNRjNw4MC1a9caDAalawFEXFxcpUqVlK4CgHqRpgAA6jJ06NDExMSdO3cqXQhKuszMzHv37lWsWFHpQgCoF2kKAKAu1apVa9WqFTf7QXG3bt0SQjA2BcAM0hQAQHUGDx68ZcuWlJQUpQtBiRYXFydIUwDMIk0BAFQnODjYwcFh3bp1SheCEu3WrVtarbZChQpKFwJAvUhTAADV8fDw6N69Ozf7QVlxcXE+Pj5arVbpQgCoF2kKAKBGgwcPPnTo0J9//ql0ISi5bt26xW1+AMwjTQEA1Khjx45+fn7h4eFKF4KSi+nRARSJNAUAUCMHB4e33norLCzMaDQqXQtKqFu3bjE9OgDzSFMAAJUaOnTo9evXDx48qHQhKKEYmwJQJNIUAEClGjRo0KRJk9WrVytdCEqo+Ph4xqYAmEeaAgCo15AhQ9avX5+enq50IShx7t+/n5mZ6e3trXQhAFSNNAUAUK8BAwZkZWVt2bJF6UJQ4iQmJgohvLy8lC4EgKqRpgAA6lW+fPlOnTpxsx+ev+TkZCGEp6en0oUAUDXSFABA1YYMGRIdHX3z5s3cjffv31eqHpQQcppibAqAeaQpAICqde3atVy5chEREUKIuLi4OXPm1KtXb968eUrXBTuXnJzs4uLi6uqqdCEAVE2ndAEAAJjj5OTUq1evBQsW7N69e8+ePVqtNicnx2AwKF0X7FxycjIDUwCKRJoCAKjX8ePHV69eHRERodfrExISjEaj0Wh0dnYmTaG43blzh4emABSJNAUAUB1Jkj7//PPFixfHxsY6OTllZWUJIXJyckwbZGdnK1cdSoTk5GTSFIAi8dwUAEB1NBpNpUqVYmNjhRBylMpNkiTSFIobaQqAJUhTAAA1Gjx48KRJk7Rabf5VkiTlHqcCigNpCoAlSFMAAJX697///frrrzs6OuZfxdgUiltSUhJpCkCRSFMAAJVycHD48ccfa9asmSdQSZLELBQoboxNAbAEaQoAoF7u7u47duxwc3NzcHj0C4vnplDccnJy7t27R5oCUCTSFABA1apVq/bTTz/lTlNGo5GxKRSr+/fvG43GF154QelCAKgdaQoAoHatW7detmyZaZGxKRS3tLQ0IYS7u7vShQBQO9IUAMAGvPPOOyNHjjRN8UeaQrEiTQGwEGkKAGAbFixY0Lp1a3lGiocPHypdDuxZenq6EKJ06dJKFwJA7UhTAADboNPpNmzYUKlSJcHYFIoZY1MALKRTugAAgDl9+/ZVugR1qVOnTmxs7KlTp7gyz8r69euVLkF1GJsCYCHGpgBA1aKiomJjY5WuQkU8PDyCgoI0Go3ShdiD2NjYqKgopatQo7S0NGdn5wLfHA0AuTE2BQBqN3HixODgYKWrUJcDBw60adNG6Sps3rp16/r166d0FWqUnp7OwBQASzA2BQCwPUQpFKu0tDQemgJgCdIUAADAYxibAmAh0hQAAMBj0tPTGZsCYAnSFAAAwGPS0tIYmwJgCdIUAADAYxibAmAh0hQAAMBjGJsCYCHSFAAAwGP0er2bm5vSVQCwAaQpAACAx2RnZzs5OSldBQAbQJoCAAB4jMFg0Ol0SlcBwAaQpgAAAB5DmgJgIdIUAADAY0hTACxEmgIAAHgMaQqAhUhTAAAAjzEYDFqtVukqANgA0hQA2KG0tDS7PBbwfGRnZzM2BcASpCkAsCvLli1r27Zt/fr17exYz1xKSsq0adOmTp1q4fYbN25s166dRqPRaDQtWrRo1aqVv79/YGDglClTLl++XKyl4vkzGAyOjo5KVwHABpCmAMCuDB8+3Gg05uTk2NmxLBEfH2/hluvWrQsJCZk9e3Z6erqFu/Tq1WvNmjVCiKpVq/72228xMTEnTpxYuHDh6dOn69at+9FHHxmNRivrftYsvw4oDM9NAbAQaQoA7IpWq/Xz87O/YxUpJSXl7bfftnDj4ODglStXPukh3N3dhRAuLi6mlmbNmm3btq1fv36fffbZ559//qQdFocnug4oDGkKgIVIUwAAm6fX64ODg69du2b5Ls7Ozk96FI1Gk7/RwcFh8eLFFSpUmD179vXr15+0z2fLiuuAApGmAFiINAUA9mDLli0hISFTpkwZN26c6UavkydPTp48uUaNGnq9fvjw4Z6engEBAVeuXDG/yrpjxcXFff755y+99NLdu3dff/31qlWr3rlzRwgRFRU1duzYSZMmde7cedq0aQ8fPhRCnD9//qOPPmrQoEFcXFz37t3LlSsXEBBw5MgR0yEK3CsyMtLd3b1y5cpCiNTU1FmzZmm12qCgICHEpk2bLl68mJycPGLEiC+++MLqyxgTE1O5cuUdO3Y80V5lypQJDg7OyMiIjIy0j+sA0hQAS0kAABUTQkRGRprfJjw8PDAwMDMzU5Kk5ORkLy8vHx8fSZLi4+M7dOgghBg9evS5c+dOnDjh7Ozcv39/86usO9aOHTvq1aun1Wo/+eST5cuXBwQExMXFffXVVy1btszKypI3rl27dps2bYxG44EDBxo0aKDVaidMmLB3794NGzaUL1/e1dX11q1bkiQVtpckSR07dvTz8zMV07Bhw8DAQPlz165dq1WrZvmFffDggRBizJgxuRu3bdvm4uISHh5e4C4pKSlCiHr16uVftXbtWiHE0KFDbes6REZG8k2gQK6urqtWrVK6CgA2gP9DAUDVikxTer3e19c3IiLC1NKrVy854UiSJM9Zl5ycLC+2atWqdu3aRa6y7ljvvvuuEOLSpUvyYkJCgpub25o1a0wbr1q1SggRFhYmSdKwYcN0Op0cFaT/fa3/+OOPze/Vo0eP3CkiMDDw2aYpSZIMBkNhu5hJU7/++qsQon379rZ1HUhThXFycsp98QGgMNzpBwC27eDBg/Hx8Q0bNjS1ODk5mT7LbyA13bPk5+dnej2UmVXWHcvR0VGn09WsWVNePHLkiF6vl29Ik3Xt2lUIsW/fPvnoOp3ONAl1z549nZyczpw5Y36v58C6d7ampqYKIerUqSPs5TqUcNzpB8BCpCkAsG0XL14UQjyfd+M80bHkKRnu3r1ravH09JRvY8u/saOjY8WKFQ0GwxPtpR4XLlwQQjRu3Dj/qhJ1HeyG0Wh0cOA7EoCi8T8FANg2eXTo+cwm90THql69uhAi/8wWdevWLXD7rKysunXrPuleaiBJUlRUlKOjY6dOnfKvLTnXAQBKINIUANi2Ro0aCSHWr19vaim+N+o+0bECAwPd3d03b95saomLi8vIyOjWrVv+jRMTE2/fvt27d2/ze+l0uvT0dNMR09PTTe/MdXBwsPxVvGaYeQmvJEkFtn/55ZdnzpyZMmVK1apV86+10esAALAEaQoAbFvLli3btm27atWqpUuXZmRk/P777zExMUlJSRERERkZGfLzPAaDQd44ISFBno5P/O9RnwJXWXcs+fu9PFWDEMLT0zM0NPTQoUPR0dFyy4IFCwYNGvSPf/xDXnz48OGZM2fkz7Nnzx44cGBgYKD5vRo2bJiSkhIaGvrXX399+umnDx8+/Ouvv/744w8hRMWKFZOTk48fP75///6MjIwir5u8TZ4ouGvXrrJly0ZFRRW4i3zFcnd+/fr1cePGffjhh+PHj585c6bcaFvXAQDwNLQzZsxQugYAQKFmzpzZt2/fF198JWmumQAAIABJREFU0cw2PXv2vH379ooVK5YuXVq6dGlfX99GjRoFBQXFxsZ++eWXKSkp6enpAQEBmzdvXrZsWVpamkajMRgMha1q3bq1mSdGCjvW/v37V6xYodfrb9++Xa1aNR8fHyFEQEBA48aN58+ff+zYsaNHj5YrV27OnDnyO3B//vnnM2fOZGRkrF27dteuXT4+Pl999ZW8ysxe/v7+586dW7t27W+//TZu3LjExMS6detWrVq1du3aVapU2bp1608//RQYGFjg80u5/fLLL6GhoWfPnr1z546rq2vFihXd3d2FEDdu3Ni6dWu3bt3q1auXZ5ddu3Z98sknf/31V2pqanR0dERERGRk5NatW729vZcuXTpkyBC5wpUrVy5fvtxWrsO5c+eioqL4JpCfJf/uAEAIoTH/Z0gAgLI0Gk1kZGRwcLDShTxjI0aMWLt2bWZmptKFKEzZ67Bu3bp+/frxTSA/e/13B+CZY/ZPAMBj/Pz8Hj58WOCqsLCwzp07P+d6rGAHpwAAsAmkKQDAY2JjY5/DUe7evZuVlZWenl66dOln3vnzOYVnolivAwCguDELBQDgeZs6deqvv/5qNBrHjRsXExOjdDmK4ToAgK1jbAoA8LyFhoaGhoYqXYXyuA4AYOsYmwIAAAAAa5CmAAAAAMAapCkAAAAAsAZpCgAAAACsQZoCAAAAAGuQpgAAAADAGqQpAAAAALAGaQoAAAAArEGaAgAAAABrkKYAAAAAwBqkKQAAAACwBmkKAAAAAKxBmgIAAAAAa+iULgAAUIR58+atX79e6SpslSRJGo1G6SpUKjY2VukSAMC2MTYFAKrWp08fPz8/pauwVdu3b79+/brSVaiXn59fnz59lK4CAGwYY1MAoGqMSj2NgICAoKCg+fPnK10IAMA+MTYFALBb/v7+J06cULoKAIDdIk0BAOyWnKaMRqPShQAA7BNpCgBgt/z9/dPT0y9fvqx0IQAA+0SaAgDYrUaNGul0Om72AwAUE9IUAMBuubi41K1blzQFACgmpCkAgD1jIgoAQPEhTQEA7BlpCgBQfEhTAAB75u/vn5iYeOvWLaULAQDYIdIUAMCe+fv7azQahqcAAMWBNAUAsGdly5atWrXqH3/8oXQhAAA7RJoCANg5Hp0CABQT0hQAwM6RpgAAxYQ0BQCwc/7+/tevX797967ShQAA7A1pCgBg55o0aSJJ0qlTp5QuBABgb0hTAAA7V7FiRW9vb272AwA8c6QpAID9e/nll0lTAIBnjjQFALB/TEQBACgOpCkAgP3z9/e/ePFiRkaG0oUAAOwKaQoAYP/8/f1zcnLOnj2rdCEAALtCmgIA2L9atWp5eHj88ccfShcCALArpCkAgP3TaDSNGjXi0SkAwLNFmgIAlAhMRAEAeOZIUwCAEsHf3//MmTPZ2dlKFwIAsB+kKQBAidCkSZMHDx78+eefShcCALAfpCkAQInQoEGDUqVKcbMfAOAZIk0BAEoER0fHBg0akKYAAM8QaQoAUFIwEQUA4NkiTQEASgo5TUmSpHQhAAA7QZoCAJQU/v7+qampV69eVboQAICdIE0BAEqKxo0bOzg4cLMfAOBZIU0BAEoKNze32rVrk6YAAM8KaQoAUII0adKENAUAeFZIUwCAEiT3tH5XrlzZsGHD/PnzlS0JAGC7SFMAgBLBYDCcOXMmNTVVq9W2aNHC3d29Zs2affr02bBhg9KlAQBslU7pAgAAKEZGo3HChAkHDx48f/58VlaWg4ODk5NTbGysvFan09WvX1/ZCgEAtos0BQCwZw4ODq6uridPnpQXjUbjgwcPcq+tU6eOQqUBAGwed/oBAOzctGnTvLy8NBpN/lVZWVmkKQCA1UhTAAA7V7p06blz5xa2ljQFALAaaQoAYP8GDx7cpEkTnS7v/e1arbZ69eqKlAQAsAOkKQCA/dNoNMuWLcvJycnTXqlSJScnJ0VKAgDYAdIUAKBEaNq06cCBAx0dHXM3NmjQQKl6AAB2gDQFACgp5s6dmztNOTk5MT06AOBpkKYAACWFt7f39OnTtVqtvGg0GmvXrq1sSQAAm0aaAgCUIO+//36VKlUcHByEEAaDgQn9AABPgzQFAChBnJyc5s2bZzQa5UXSFADgaZCmAAAlS/fu3V999VWNRuPs7Ozn56d0OQAAG5b3zRsAgBJo3bp1SpfwXHXt2nX//v3ly5dfv3690rWoSIsWLYiXAPBENJIkKV0DAEBhGo1G6RKgvMjIyODgYKWrUAWNRsPVAGAJ7vQDAAghRGRkpFSS3L17d968eUpXoSJK/wACgE0iTQEASqIXXnhhzJgxSlcBALBtpCkAQAml0/HwMADgqZCmAAAAAMAapCkAAAAAsAZpCgAAAACsQZoCAAAAAGuQpgAAAADAGqQpAAAAALAGaQoAAAAArEGaAgAAAABrkKYAAAAAwBqkKQAAAACwBmkKAAAAAKxBmgIAWCktLc0ujwUAgIVIUwCAJ7Zs2bK2bdvWr1/fzo5VpIiIiFdeecXDw6N58+bbt2+3ZJeNGze2a9dOo9FoNJoWLVq0atXK398/MDBwypQply9fLu6CAQDFijQFAHhiw4cPNxqNOTk5dnYs8+bNmxceHj5o0KB333333LlzXbt23b17d5F79erVa82aNUKIqlWr/vbbbzExMSdOnFi4cOHp06fr1q370UcfGY3G4q8dAFAsSFMAgCem1Wr9/Pzs71hmpKenb9u2bdu2bePHj583b150dLRGo5k7d64l+7q7uwshXFxcTC3NmjXbtm1bv379Pvvss88//7y4igYAFDPSFAAARTt69GhoaKhpsXnz5k2aNLl06ZIl+2o0mvyNDg4OixcvrlChwuzZs69fv/7MCgUAPEekKQCApbZs2RISEjJlypRx48bFx8fLjSdPnpw8eXKNGjX0ev3w4cM9PT0DAgKuXLlifpV1xxJCSJK0dOnSkSNHNm/evGPHjv/973+LPNCpU6eGDh06Z86ciRMnjho1ykw/ZrRv375Zs2a5Wzw8PKpVqyZ/jomJqVy58o4dOyw5NZMyZcoEBwdnZGRERkYqeGoAAOtJAIASTwgRGRlpfpvw8PDAwMDMzExJkpKTk728vHx8fCRJio+P79ChgxBi9OjR586dO3HihLOzc//+/c2vsu5YkiSFhoZ+//33kiQZDIYGDRr4+Pjo9XrzB6pbt25MTIwkSRkZGa1btzbTj+VXzGAweHl5fffdd/Litm3bXFxcwsPDC9w4JSVFCFGvXr38q9auXSuEGDp0qOKnZsnPQMnB1QBgIdIUAKDo7456vd7X1zciIsLU0qtXL1PCmTp1qhAiOTlZXmzVqlXt2rWLXGXFseLi4ry9vXNycuT2jz/+WAjx448/mjlQVlaWEGLhwoVyu9ytmX4stHHjxtdee81oNJpaDAZDYRubSVO//vqrEKJ9+/aKnxr5ITeuBgAL6Z7fKBgAwGYdPHgwPj6+YcOGphYnJyfTZ61WK4TQ6f7+neLn52d6oMjMKiuO9dtvv2VnZ7/33numVcOHD5dndyjsQI6Ojh07dhw/fvz58+c/++yzt956y3w/lrh3796nn366ffv23A9EyQU8qdTUVCFEnTp1VHJqAIAnQpoCABTt4sWLQghHR0dlj3XhwgU3N7cVK1Y8UYcbN24cMWLEkiVLNmzYsH79+jZt2ljXj8nEiRPnzZvn7e1t3e65XbhwQQjRuHFjlZwaAOCJMAsFAKBo8ujQ85l6zsyxXF1dY2NjY2NjczcmJSWZ79DR0TEiIkJ+6VPHjh0vXrxoXT+yRYsW9ejRo02bNpZsbJ4kSVFRUY6Ojp06dVLDqQEAnhRpCgBQtEaNGgkh1q9fb2opvjfqmjlWw4YNJUmaMmWKadXly5cXL15spreHDx8uWbJECDFw4MAjR44YjcY9e/ZY0Y8sIiLCxcWlR48ephbTC3zNvIRXkqQC27/88sszZ85MmTKlatWqip8aAMAK3OkHAChay5Yt27Ztu2rVqqZNmw4ePPjcuXMxMTFJSUkRERE9evSQH/4xGAzyxgkJCfJ0fBqNxswqK47VvXv3Zs2aRUREPHjwoGfPnvfv39+4ceOPP/4o/vcAUv4DCSG+/fbbMWPGyG8BLlOmjL+/f2BgYGH9mLF9+/aFCxcOHTp02bJlQghJks6cOVO/fv0OHTrs2rWrd+/e3333XZ8+ffLvKNeWkZFharl+/fqXX375zTffjB8/fubMmUKI1157TcFTAwBYSaHZLwAAKiIsmMEsJSVl2LBh3t7eVapUmTFjRkhIyLBhw3bv3r179+7q1asLIUaNGpWYmLh27Vo3NzchxIwZM3bt2lXYKjMz4Jk5Vk5Ozp07dwYMGFChQgUvL6/BgwfHxcVJkrRnz57CDqTX65s1a/bGG2/MmTMnJCRk+fLl8iEK7MeMY8eO5Z/LwdnZ+c6dO3IBvr6+mzdvzr/jzp07u3btKm/fqlWr9u3bd+nSpXPnzu+///6pU6dyb6nUqcks+RkoObgaACykkQq5/QAAUHJoNJrIyMjg4GClC4Fi+BnIjasBwELc6QcAUICfn9/Dhw8LXBUWFta5c+fnXI9MnVUBAFSLNAUAUECeSedUQp1VAQBUizn9AAAAAMAapCkAAAAAsAZpCgAAAACsQZoCAAAAAGuQpgAAAADAGqQpAAAAALAGaQoAAAAArEGaAgAAAABrkKYAAAAAwBqkKQAAAACwBmkKAAAAAKxBmgIAAAAAa5CmAAAAAMAapCkAAAAAsAZpCgAAAACsoVO6AACAKhw+fFjpEgAAsDGkKQCAEEJ8/fXXX3/9tdJVAABgS0hTAAAhSZLSJajCyJEjL168uHfvXqULAQDYBp6bAgDgbz4+PvHx8UpXAQCwGaQpAAD+5uvre/v2baWrAADYDNIUAAB/8/X1TU1NzcjIULoQAIBtIE0BAPA3Hx8fIQTDUwAAC5GmAAD4m6+vryBNAQAsRpoCAOBv3t7eDg4OTEQBALAQaQoAgL85OjqWK1eOsSkAgIVIUwAAPOLr68vYFADAQqQpAAAe8fHxYWwKAGAh0hQAAI8wNgUAsBxpCgCAR3iBLwDAcqQpAAAe8fHxYWwKAGAh0hQAAI/4+PgkJibm5OQoXQgAwAaQpgAAeMTX1zcnJyc5OVnpQgAANoA0BQDAIz4+PkIIbvYDAFiCNAUAwCO+vr6CNAUAsAxpCgCARzw8PNzc3JjWDwBgCdIUAACPYVo/AICFSFMAADzGx8eHsSkAgCVIUwAAPIYX+AIALESaAgDgMdzpBwCwEGkKAIDHcKcfAMBCpCkAAB7j6+vL2BQAwBKkKQAAHuPr66vX69PS0pQuBACgdqQpAAAe4+PjI3iBLwDAAqQpAAAe4+vrK4Tg0SkAQJFIUwAAPMbLy0ur1TI2BQAoEmkKAIDHaLVaLy8vxqYAAEUiTQEAkBcv8AUAWII0BQBAXrzAFwBgCdIUAAB58copAIAlSFMAAOTFnX4AAEuQpgAAyIs7/QAAltApXQAAAGqRlZWVkJAQFxcXGxubnJz88ccfJyYmxsXFxcXFVahQ4ZdfflG6QACAupCmAAAQqampDRo0uHXrlqlFp9PNmTPHaDRmZ2drNJpRo0YpWB4AQJ240w8AAFGmTJkuXbo4ODz6tWgwGB4+fJidnS0vtm3bVqHSAADqRZoCAEAIISZPnixJUmFr27Rp8zyLAQDYBNIUAABCCFGnTp0333zT0dEx/6rq1at7e3s//5IAACpHmgIA4G8fffSR6dY+E0dHx9dee02RegAAKkeaAgDgbwEBAS1atNDpHpuiKScnh4emAAAFIk0BAPDI1KlTDQZD7haj0diqVSul6gEAqBlpCgCAR9544426devmntyvSpUqlStXVrAkAIBqkaYAAHhEo9H8v//3/0yLOp2uffv2CtYDAFAz0hQAAI8ZMGCAaQY/SZLatWunaDkAAPUiTQEA8BhHR8f3339fq9UKpqAAAJhFmgIAIK/33nvPxcVFCOHr61u1alWlywEAqBRpCgCAvNzd3ceOHSuE6NChg9K1AADUSyNJktI1AAAsotFolC4BeK6U+pai0WgiIyODg4MVOToAG6IrehMAgGpMmDAhKChI6SpKiuXLl3fr1s3Hx0fpQkqiw4cPf/3110pXAQBFIE0BgC0JCgri7+XPTYsWLfz8/JSuouQiTQFQP56bAgCgYEQpAIB5pCkAAAAAsAZpCgAAAACsQZoCAAAAAGuQpgAAAADAGqQpAAAAALAGaQoAAAAArEGaAgAAAABrkKYAAAAAwBqkKQAAAACwBmkKAAAAAKxBmgIAAAAAa5CmAAAAAMAapCkAsHNpaWk22rnapKenK10CAEBdSFMAYLeWLVvWtm3b+vXrF0fnixYtat26dWBg4LPtNiIi4pVXXvHw8GjevPn27dst2WXnzp2DBw/WaDQajSYoKKh9+/ZBQUHNmzefN2/es8o/4eHhHTp0qF27dv5VP/7444svvqjRaFq3bm0wGEzt9+7dmzVrloeHh5ub2/Tp0+/cufNMKrGcwWA4ePDgRx999Ouvv8ote/bsCQwMvHbt2jPp/9n2BgA2ijQFAHZr+PDhRqMxJyenODp/7733UlNTjUbjM+xz3rx54eHhgwYNevfdd8+dO9e1a9fdu3cXuVfHjh2///770qVLCyEOHToUHR19+PDhcePGTZo06c0338zOzi6yh/j4ePMb9O/fPycnJ3dYyr1q3759Op0uJibmo48+MrW/8MIL06dPf+edd4YNGzZr1qzy5csXWcaz9fvvv69ateqzzz6LjY2VW+7du3fz5k29Xm91n7kv1NP3BgB2gDQFAHZLq9X6+fkVU+c6na5SpUrPsMP09PRt27Zt27Zt/Pjx8+bNi46O1mg0c+fOtWRfBweHMmXKyB/klgEDBvTu3Xvfvn2HDh0yv29KSsrbb79tfhvzV9LLy8vb27tMmTJz587dunVr7lXVq1evUaOGJafwzAUFBY0dOzZ3S+/evePi4l588UXrOsxzoZ6yNwCwD6QpAIAqHD16NDQ01LTYvHnzJk2aXLp0ycLdNRpNnpa6desKIa5cuWJmL71eHxwc/PS3q3l5ea1evVoIMWTIkBs3bpjaS5Uq5ezs/JSdW83JyelZdfWsLhQA2BnSFADYmy1btoSEhEyZMmXcuHGmW7NOnjw5efLkGjVq6PX64cOHe3p6BgQEyEnDzCpLREdHd+zYsVy5cq+//vrTdNi+fftmzZrlbvHw8KhWrZr8OSYmpnLlyjt27LD8Ohw5csTBwcHUZ0JCwogRI2bNmjVixIiePXvKDzJt2rTp4sWLycnJI0aM+OKLL+Qtt2/fPmrUqPHjxwcFBa1YsSJ3n7dv3+7Ro0e5cuWaNGly/vz53Ku6d+8+derUu3fvBgcHF3Z7YVRU1NixYydNmtS5c+dp06Y9fPhQCBEXF/f555+/9NJLd+/eff3116tWrXrgwIF//etfdevWjY2NnTFjRpUqVV588cW9e/c+ePBg4sSJNWvWrFKliulpqMJOLb+kpKRvvvnm6NGj8mLZsmWHDRs2ceLEiRMn1qlTR6PRhIeHW36h8vRW2Nk95U8XAKidBACwEUKIyMhI89uEh4cHBgZmZmZKkpScnOzl5eXj4yNJUnx8fIcOHYQQo0ePPnfu3IkTJ5ydnfv3729+lXmdOnUqX778O++888svvyxevNjV1bVixYrp6elWd5ibwWDw8vL67rvv5MVt27a5uLiEh4cXtr18J96FCxfOnj27a9euwYMHe3h4LF682LRBu3bt+vXrJ39u3LjxwIED5c9du3atVq2aabOwsLC33norJydHkqTZs2cLIaKjoyVJGjhwoJub27hx4y5cuHD69Gk3N7c33njDtNfLL78sSVJOTo584hMnTpTbly5d+s0338ifv/rqq5YtW2ZlZUmSlJycXLt27TZt2hiNxh07dtSrV0+r1X7yySfLly8PCAg4efLkoEGDhBAhISHHjx+/f/9+8+bNa9SoMWbMmPPnz6elpbVo0aJGjRpFntrZs2eFECtXrpQkKSYmpnXr1kKIqKgoea2pyMuXL5cqVapVq1ZGo9HCC5W/t8LOzuofhsjISAW/pVjybw0AJEkiTQGAzSjyG55er/f19Y2IiDC19OrVS05TkiRNnTpVCJGcnCwvtmrVqnbt2kWuMqNTp04VK1Y0LU6bNk0IMX/+fKs7zG3jxo2vvfaa/P1eZjAYzGwvp6lBgwb17NmzUaNGDg4OgwYN+uOPP0wbtGvX7rPPPpM/DxgwoFGjRvLn3CEhMTGxTJkyV65cMS326tXr/PnzkiQNHDiwTJkyclqQJOnVV1/19fU1dS6nKUmSkpKSKleuLITYuHGjlCtNJSQkuLm5rVmzxrTLqlWrhBBhYWGSJL377rtCiEuXLpnWLlq0SAhx+vRpefGTTz4RQpw4cUJenD59uhAiMTHR/KnlTlOSJO3cudOUf4xG47Vr1+T2Ll266HS6M2fOWH6h8vRm/uys+2EgTQGwCdzpBwD24+DBg/Hx8Q0bNjS15H5yRqvVCiF0Op286OfnZ3pblJlV5nl4eJg+Dxs2TAhx/Pjxp+lQdu/evU8//XTNmjW5n4aS+zQvLCxs48aNp06d+v333w8cOBAQEPDzzz/Lq/bu3Tt16tTMzMyVK1ceO3YsIyMj/+4xMTFGo7F69eryopeX14YNG0xTzDs6Ojo6Osqfa9Soce/evfw9eHp6btiwwdnZ+Z133rl69aqp/ciRI3q9Xg5asq5duwoh9u3bJ/es0+lq1qyZ52RNk2rIWdF09CpVqgghkpOTLT81IYSrq6vps0ajqVq1qhBi06ZN27dvf//991966SWrezN/dk/5wwAAakaaAgD7cfHiRZHrO/dzVq1aNScnp8zMzKfvauLEifPmzfP29ra6hyZNmsyZM8dgMHzwwQdyS05OTmho6JAhQ+rUqdO8efMC9zp79mx2drYkSUX2n3/SC5NmzZotWLAgJSWlb9++Dx48kBuvX78uhLh7965pM09PT1dX11u3bllyOnkOJy+apqe35NQKpNfrJ0yYUKVKlY8//tjUaEVvT3l2AGC7SFMAYD/kkSj5q+3z5+DgoNPpTEMcVlu0aFGPHj3atGnzlP00bdpUCHHp0qXs7Gyj0dilS5czZ86sW7fOTM8eHh4PHjzIM72EPJvCEwkJCRk6dOjx48f//e9/yy3yeFf+2RfkiQefhoWnVqBZs2bduHFjwYIFbm5uT9Nb8Z0dAKgcaQoA7EejRo2EEOvXrze1FN/be/O7du1adnZ2cHDw03QSERHh4uLSo0cPU4vpBb7m3xScf0Dpzz//FELUqlXL0dHx2LFjO3fubN++vbwq9wCUg4NDenq6/PmVV14RQkyfPt10rOPHj//www/ma5YkKf+ta0uWLPH39zfNqRgYGOju7r5582bTBnFxcRkZGd26dTPfeZHMnJp558+f/+qrr958883u3bvLLTt37rTwQuVRfGcHACpHmgIA+9GyZcu2bduuWrVq6dKlGRkZv//+e0xMTFJSUkREREZGRmpqqhDCYDDIGyckJMhT/wkhzKwyQ6vV3rt3T6/XCyEkSZo1a9Ynn3xSr149qzvcvn37woULs7Ozly1btmzZsqVLl44ePVq+fXHXrl1ly5aNiooqcEdJkuQjysUIIa5duzZhwgQhxKeffir+d2vc6tWrz5w58/33358/fz4hIeH06dMJCQkVK1ZMTk4+fvz4/v37/f39O3fuvGnTpg4dOixatOjDDz+cOXPmwIEDhRBJSUkpKSlZWVmmM3r48KH8TNHNmzdv3bpluqlPVqpUqQ0bNrzwwgvyoqenZ2ho6KFDh6Kjo+WWBQsWDBo06B//+IcQIj09PScnJyUlxbT7/fv3c19A+eySkpLkRTm8yYNmZk4tzzVJSEgQuZ62Gj16tKOj44IFC+TF7OzsnTt3WnihMjIycvdm/uys+2EAANugzOQXAIAnJyyYZywlJWXYsGHe3t5VqlSZMWNGSEjIsGHDdu/evXv3bvl2rFGjRiUmJq5du1a+uWvGjBm7du0qbJX5afROnTrVv3//Tp06hYSEjB8/3jRZ9p49e6zo8NixYy4uLnl+STk7O9+5c0fu09fXd/Pmzfl3jI6OHj58uLx9gwYNOnXqFBAQULNmzTfffPPgwYOmzf75z3+6u7sHBgbu3r1727Ztnp6effr0SU9PP3XqlJ+fX506ddavXy9Jkl6vHzlyZKVKlby9vUeOHJmSkiJJ0po1a8qWLSuEmDhxYmpq6urVq+WY9P7770dGRrZr104I0bNnz9yHk23btm3RokWmxU2bNnXs2HHMmDEff/zxF198Ic9YuGLFCi8vLyHEkCFD5Fn79uzZIw8zDhgw4NKlS/v373/55ZeFEF26dDl9+vShQ4eaNGkir718+XJhp7Znz55OnToJIZo2bbp9+/a9e/fKdQYEBOzatUueMa9+/fqTJk2aNGnSyJEjGzduPHr0aAsvVJ7ezJyddT8MEnP6AbARGok/DgGAjdBoNJGRkU95Kx1gE9atWye/9kqRo/NvDYCFdEoXAABQLz8/v8LmYAgLC+vcubPiHQIAoCDSFACgULGxsSrvEAAABTELBQAAAABYgzQFAAAAANYgTQEAAACANUhTAAAAAGAN0hQAAAAAWIM0BQAAAADWIE0BAAAAgDVIUwAAAABgDdIUAAAAAFiDNAUAAAAA1iBNAQAAAIA1SFMAAAAAYA3SFAAAAABYgzQFAAAAANYgTQEAAACANXRKFwAAeAL9+vXr16+f0lUAAAAhSFMAYEMiIyOVLqGE+s9//jN37tzw8HCdjt+bAIBH+K0AADYjODhY6RJKKC8vr7lz53bs2NHT01PpWgAAKsJzUwAAFMHDw0MIcf/+faULAQCoC2kKAIAikKYAAAUiTQHAVAxFAAAgAElEQVQAUATSFACgQKQpAACKQJoCABSINAUAQBFcXFycnJxIUwCAPEhTAAAUzd3dnTQFAMiDNAUAQNE8PDxIUwCAPEhTAAAUzcPDIy0tTekqAADqQpoCAKBojE0BAPIjTQEAUDTSFAAgP9IUAABFI00BAPIjTQEAUDTSFAAgP9IUAABFI00BAPIjTQEAUDTeNwUAyI80BQBA0RibAgDkR5oCAKBopCkAQH6kKQAAiubh4aHX63NycpQuBACgIqQpAACKVqZMGUmS0tLSlC4EAKAipCkAAIrm4eEhhEhNTVW6EACAipCmAAAompymeHQKAJAbaQoAgKKRpgAA+ZGmAAAoGmkKAJAfaQoAgKK5ublptVrSFAAgN9IUAABF02g07u7upCkAQG6kKQAALMILfAEAeZCmAACwiIeHB++bAgDkRpoCAMAijE0BAPIgTQEAYBHSFAAgD9IUAAAWIU0BAPIgTQEAYBHSFAAgD9IUAAAWIU0BAPIgTQEAYBHeNwUAyEOndAEAANgGR0fHe/funT9/Pi0t7f79+ykpKaVKlXrzzTeVrgsAoBjSFAAABfvzzz+7det27949vV6fkfH/2bvzuCbOxI/jT0gQAW/Ba6HeUrXqeqAgXtXiFqWeFE+stp61qN3ata1ara7Ful3Zine9FSyHV7voKt6i4rVe9VxrtYosgoorh0DI/P6Y/UVWIYYBmSR83n8lM8kz3zwvXsqXZ2aSKW9s3ry58QUTJ06kTQFAWUabAgCgYB4eHlqtNjU1VZKkAl/g5+dXypEAABaF66YAAChUcHCwnV3B/1eWK1fuzTffLOU8AACLQpsCAKBQQUFBDg4OL263s7N78803nZycSj8SAMBy0KYAAChUhQoVRo0aZW9v/9x2Ozs7f39/VSIBACwHbQoAAFMmTpyo1+uf26jX67loCgBAmwIAwJSmTZt6e3trtdr8Gxs0aNCwYUO1IgEALARtCgCAlwgODjYYDMan5cqV69+/v4p5AAAWgjYFAMBLDBw40MXFxfg0JyeH0/wAAII2BQDAS9nb248dO9Z4LwpHR8dOnTqpGwkAYAloUwAAvNy4cePy8vKEEFqt1tfXt8DbpgMAyhraFAAAL+fu7u7n56fT6YQQ3BsdACCjTQEAYJaPPvpIr9cbDIZevXqpnQUAYBF0agcAALwqx48fX7hwodopbIckSU5OTjqdbsqUKWpnsU3R0dFqRwCAomFtCgBs1p07d2JiYtROYTs0Gk2jRo3q1KmjdhAbdPfuXX5WAVgj1qYAwMbx9/4S9ODBg5s3b3p6eqodxNZERUUNGjRI7RQAUGS0KQAAzFW9evXq1aurnQIAYCk40w8AAAAAlKBNAQAAAIAStCkAAAAAUII2BQAAAABK0KYAAAAAQAnaFAAAAAAoQZsCAAAAACVoUwAAAACgBG0KAAAAAJSgTQEAAACAErQpAAAAAFCCNgUAAAAAStCmAADPe/LkidoRAACwArQpAMAzK1as6Nq1a9OmTdUOUrD9+/d7eXndunXLooYqTERERLt27SpVqtShQ4edO3cqG6R3794pKSnK3mtd0wUA1og2BQB4ZvTo0QaDIS8vT+0gzyQlJRkfP3r06M6dOxkZGaoP9VKhoaHh4eFBQUEffPDBpUuX/P399+7dW9RBrl27tnPnzu+//978t1jpdAGAlaJNAQCe0Wq1bm5uaqd4Ji0tbejQocanAwcOTExMbN68ubpDvVR6enpsbGxsbOzkyZNDQ0P37dun0Wj+8pe/FHWc5cuXly9fftmyZXq93pzXW+l0AYD1ok0BACxURkZGYGBgiZxdVoJDmePEiRMhISHGpx06dGjTps2NGzeKNEhWVtbx48enTp169+7d7du3v/T11jtdAGC9aFMAALFjx46xY8dOmzZt0qRJxvO7zp079+mnnzZo0CAjI2P06NEuLi7t27e/efOm6V2mJScnjxkzZu7cuWPGjOnfv/+DBw+Mu3bu3Pnhhx9OnjzZ29tbPrdt27ZtV69eTU1NHTNmzLfffiuESElJWbx48YkTJw4fPuzq6qrRaGbMmCG/fd++fZUqVZo1a1aBhzAxlDFATExMcHDw1KlT/fz8ZsyYkZ2drfiT9ujRw9PTM/+WSpUq1atXT34cHx/v7u6+a9cu04Ns3rw5MDBw3LhxWq02LCzsub22NF0AYMUkAICNioyMNOff+fDwcC8vr6ysLEmSUlNTXV1da9WqJUlSUlLSW2+9JYSYOHHipUuXzp496+DgMHjwYNO7TOvWrdugQYPkx61atRo+fLj8eMOGDUOGDMnLy5Mkad68eUKIffv2SZLk7+9fr149+TXx8fGdO3cWQsTExEiSJP+Wv3XrVnlvbm5u586dDQZDYYcwMZQkSQsXLvTx8cnJyZEnoXHjxl26dDEYDIo/aX56vd7V1XXNmjXy09jYWEdHx/DwcNPv6tSpU2pqqiRJ/fr1E0KcO3fOuMv2psvMn9VSI4SIjIxUOwUAK8DaFACUaZmZmVOnTp00aVL58uWFENWrV5d/bxZC1KpVS15g+eqrr5o1a/b73//e09PzzJkzpne9VKtWreQHb7zxxoULF4QQKSkpwcHB8+bNs7OzE0KMGTNmwIABtWvXfu6NPj4+M2fOND4dP358tWrVwsPD5ae7d+8ePHiwRqMp8BCmh7p///7MmTPHjx9vb28vT8IXX3xx+PDhTZs2FeeTGv3444+///3vR44cKT/t1avXkydP8l+V9KLTp0/Xr1+/evXqQogJEyYIIRYvXizvsvnpAgArQpsCgDLtyJEjSUlJLVq0MG4pV66c8bFWqxVC6HQ6+ambm5vxq6hM7DLhwIEDn3/+eVZW1qpVq06ePJmZmSmEiI+PNxgM9evXl1/j6uq6ZcuWAu/S7uTkZHzs7Ow8YsSIH3/8MTU1VQgRGRk5ZMiQwg5heqiEhISMjAx3d3fjFn9/fyHEwYMHFX9So0ePHv35z3/euHGjXF1k8pgmLF26dNy4cfJjX1/fRo0ahYeHP3z4UNj6dAGAdaFNAUCZdvXqVSGEvMhQCvLy8kJCQt57770mTZp06NBB3vjzzz/n5uZKklTU0caOHZubm7tp06a0tDStVlu1atXCDmHa7du3hRByV5G5uLg4OTndu3evqJFe9PHHH4eGhtasWdP8t6SlpcXFxU2dOtXb29vb27tjx45CiKysrNWrVwtbny4AsC60KQAo0+SVKPn341fNYDD06tXr4sWLUVFRXbp0MW6vVKnS06dPL1++nP/F8l0NTGvatGnnzp3XrFkTGRk5bNgwE4cwTV7nefFmCR4eHmaOUJglS5b069fP/CSy1atXf/rpp8fz2b9/v1arXbp0qcFgsOHpAgCrQ5sCgDKtZcuWQojo6Gjjllf37b0nT57cs2dPjx495KfGBZZ27doJIWbOnGkwGORdZ86c2bx5sxDCzs4uPT3dxJhjx469ePHihg0bunfvbuIQpofy8vKqWLFi/ruQJyYmZmZm9unTpxgfV0RERDg6Osr3kJAZv8DX+ElflJOTs3jx4ueuqnJ3d3/77bdv3br1008/2ep0AYA1ok0BQJnm4+PTtWvXtWvXLl++PDMz89SpU/Hx8SkpKREREZmZmY8fPxZCGL86Njk5Wb71nxDCxK7CyBcOrV+//uLFi+vWrbt8+XJycvKFCxcaNWrk5+e3bdu2t956a8mSJX/605+++uqr4cOHCyHq1KmTmpp65syZQ4cOZWZmJicnCyHkK39kAQEBVatW9fX1lW/JUNghkpOTTQzl4uISEhJy9OjRffv2ycMuWrQoKChIrhwKPqkQYufOnWFhYbm5uStWrFixYsXy5csnTpwon1cZFxdXpUqVmJiYAt+4fv362rVru7i4PLddvjZpzpw5HTt2tL3pAgBrpdrdBAEAr5iZd51OS0sbNWpUzZo1X3vttdmzZ48dO3bUqFF79+7du3evfE7Xhx9+eP/+/U2bNjk7OwshZs+eHRcXV9guvV5v4ljjx4+vWLGil5fX3r17Y2NjXVxcAgIC0tPTMzIyJkyY8Lvf/a5mzZoTJkxIS0uTX3/+/Hk3N7cmTZpER0cfOHCgW7duQoj27dvHxcUZx5w5c2ZSUtJLD/HSobZt29azZ8+PPvroyy+//Pbbbw0GgyRJ+/fvV/BJT5486ejo+Nx/uA4ODg8ePJDHrF279vbt219849atW11cXCpWrBgWFpZ/+7Fjx9555x15nOHDhycmJtrSdEncIR2A1dJI/LkIAGxUVFSU/FVCagcBXsLSflY1Gk1kZGRgYKDaQQBYOp3aAQAANsXNza2wOyJs2LDBz8+vlPO8OmXnkwIACkObAgCUpLt376odoZSUnU8KACgMd6EAAAAAACVoUwAAAACgBG0KAAAAAJSgTQEAAACAErQpAAAAAFCCNgUAAAAAStCmAAAAAEAJ2hQAAAAAKEGbAgAAAAAlaFMAAAAAoARtCgAAAACUoE0BAAAAgBK0KQAAAABQgjYFAAAAAErQpgAAAABACZ3aAQAAr9a7776rdgTgJe7evat2BABQgrUpALBZ7u7uAQEBaqewQffu3duxY4ckSWoHsR1ubm78rAKwRhr+MwAAoEh27tzZu3fvJ0+eVKhQQe0seCU0Gk1kZGRgYKDaQQBYOtamAAAoGicnJyFEZmam2kEAACqjTQEAUDTOzs5CiIyMDLWDAABURpsCAKBoWJsCAMhoUwAAFA1rUwAAGW0KAICiYW0KACCjTQEAUDSsTQEAZLQpAACKxtHRUaPRsDYFAKBNAQBQNHZ2duXLl2dtCgBAmwIAoMicnZ1ZmwIA0KYAACgyJycn1qYAALQpAACKzNnZmTYFAKBNAQBQZJzpBwAQtCkAABRwcnKiTQEAaFMAABQZZ/oBAARtCgAABVibAgAI2hQAAAqwNgUAELQpAAAUYG0KACBoUwAAKMDaFABA0KYAAFCAtSkAgKBNAQCggJOTE2tTAADaFAAARca39wIABG0KAAAFWJsCAAjaFAAACshrU5IkqR0EAKAm2hQAAEXm5ORkMBiys7PVDgIAUBNtCgCAInN2dhZCcLIfAJRxtCkAAIqMNgUAELQpAAAUcHJyEkJwWz8AKONoUwAAFBlrUwAAQZsCAEAB1qYAAII2BQCAAqxNAQAEbQoAAAVYmwIACNoUAAAK6HS6cuXKsTYFAGUcbQoAACWcnZ1ZmwKAMk4jSZLaGQAAsALLli07d+5cenp6dnZ2amrqhQsXKlasKElSbm6uvDE+Pr59+/Zqx0QJ0Gg0kZGRgYGBagcBYOl0agcAAMA6GAyGlStXajTP/hD56NEj414XF5d27dqpFA0AoA7O9AMAwCzDhw93cHAo8JwOe3v7AQMG2NnxvyoAlC38uw8AgFkqV648aNAge3v7F3fl5ua+8847pR8JAKAu2hQAAOYaO3Zsbm7ui9sdHBy6d+9e+nkAAOqiTQEAYC4fH58mTZpoNJr8G7Vabc+ePeVvoAIAlCm0KQAAimDChAkvXh/Vr18/VcIAANRFmwIAoAhGjhyp1WrzbzEYDL169VIrDwBARbQpAACKoEqVKgEBAcZ7UWg0mnbt2tWqVUvdVAAAVdCmAAAomvz3otDpdAMGDFA3DwBALbQpAACKpmvXro0aNZLvRZGbm9unTx+1EwEA1EGbAgCgyMaNGydfPeXu7t6sWTO14wAA1EGbAgCgyEaOHCmvTQ0cOFDtLAAA1ejUDgAAsCDHjx+/c+eO2imsQ7t27Y4fP16pUqWoqCi1s1iTjh07urm5qZ0CAEoGbQoA8MzChQtjYmLUTmFN5syZo3YEKxMZGRkYGKh2CgAoGZzpBwD4HwEBARLMYDAYZs2apXYKK6P2TzcAlDDaFAAASmg0munTp6udAgCgJtoUAAAKGb/DFwBQNtGmAAAAAEAJ2hQAAAAAKEGbAgAAAAAlaFMAAAAAoARtCgAAAACUoE0BAAAAgBK0KQAAAABQgjYFAAAAAErQpgAAAABACdoUAAAAAChBmwIAAAAAJWhTAIAS8OTJE5s8FgAAJtCmAADFsmLFiq5duzZt2tTGjlWyIiIi2rVrV6lSpQ4dOuzcudOct2zdurVbt24ajUaj0XTs2LFTp06tW7f28vKaNm3aL7/88qoDAwDMQZsCABTL6NGjDQZDXl6ejR3LHElJSea8LDQ0NDw8PCgo6IMPPrh06ZK/v//evXtf+q4BAwZs3LhRCFG3bt1jx47Fx8efPXs2LCzswoULHh4e06dPNxgMxf0AJcTMeQAA20ObAgAUi1ardXNzs71jvVRaWtrQoUNf+rL09PTY2NjY2NjJkyeHhobu27dPo9H85S9/MecQFStWFEI4Ojoat3h6esbGxg4aNOjrr7+eP3++4vAlyMx5AACbRJsCAKDIMjIyAgMDb9269dJXnjhxIiQkxPi0Q4cObdq0uXHjhjlH0Wg0L260s7NbunRpjRo15s2bd/v2bbMjvxLmzwMA2CTaFABAiR07dowdO3batGmTJk0ynuh17ty5Tz/9tEGDBhkZGaNHj3ZxcWnfvv3NmzdN71J2rMTExPnz57/xxhsPHz78wx/+ULdu3QcPHgghYmJigoODp06d6ufnN2PGjOzsbCHE5cuXp0+f3qxZs8TExL59+1arVq19+/YJCQnGQxT4rsjIyIoVK7q7uwshHj9+PHfuXK1W6+3tLYTYtm3b1atXU1NTx4wZ8+2335oI36NHD09Pz/xbKlWqVK9ePflxfHy8u7v7rl27zJkHo8qVKwcGBmZmZkZGRlrLPACAbZIAAPh/AQEBAQEBL31ZeHi4l5dXVlaWJEmpqamurq61atWSJCkpKemtt94SQkycOPHSpUtnz551cHAYPHiw6V3KjrVr167XX39dq9XOmjVr5cqV7du3T0xMXLhwoY+PT05Ojvzixo0bd+nSxWAwHD58uFmzZlqtdsqUKQcOHNiyZUv16tWdnJzu3bsnSVJh75IkqWfPnm5ubsYwLVq08PLykh/7+/vXq1evqDOs1+tdXV3XrFkjP42NjXV0dAwPDy/wxWlpaUKI119//cVdmzZtEkKMHDnSuuZBCBEZGWnmi1VkLTkBqI42BQB4xpw2lZGRUbt27YiICOOWAQMGyA1HkqTPP/9cCJGamio/7dSpU+PGjV+6S9mxPvjgAyHEjRs35KfJycnOzs4bN240vnjt2rVCiA0bNkiSNGrUKJ1OJ1cFSZIiIyOFEF9++aXpd/Xr1y9/i/Dy8ipmm9q6dauvr69cUWR6vb6wF5toU7t37xZC9OjRw7rmwVpairXkBKA6zvQDABTNkSNHkpKSWrRoYdxSrlw542OtViuE0Ol08lM3Nzfj10OZ2KXsWPb29jqdrmHDhvLThISEjIwM+YQ0mb+/vxDi4MGD8tF1Op29vb28q3///uXKlbt48aLpd5WsR48e/fnPf964cWP+C6LkaSmqx48fCyGaNGkirHAeAMBm0KYAAEVz9epVIYTx13HLOZZ8S4aHDx8at7i4uMinsb34Ynt7+zp16uj1+iK9q5g+/vjj0NDQmjVrFn+oK1euCCFatWr14i7LnwcLJ0mSKOQWIADwHNoUAKBo5NWh0rmbXJGOVb9+fSHEi3e28PDwKPD1OTk5Hh4eRX2XYkuWLOnXr1+XLl2KP5QkSTExMfb29m+//faLey18Hiyf/IVmxkVUADCBNgUAKJqWLVsKIaKjo41bXt036hbpWF5eXhUrVty+fbtxS2JiYmZmZp8+fV588f379//9738PHDjQ9Lt0Ol16errxiOnp6cbvzLWzs0tPTzfzg0RERDg6Ovbr18+4xfgFvia+hFdeJHnRX//614sXL06bNq1u3bov7rXkebAK8sdUdgYmgLKGNgUAKBofH5+uXbuuXbt2+fLlmZmZp06dio+PT0lJiYiIyMzMlK/n0ev18ouTk5Pl2/GJ/7/Up8Bdyo4l/34v36pBCOHi4hISEnL06NF9+/bJWxYtWhQUFNS9e3f5aXZ29sWLF+XH8+bNGz58uJeXl+l3tWjRIi0tLSQk5Pr163/+85+zs7OvX7/+z3/+UwhRp06d1NTUM2fOHDp0KDMz08Sn2LlzZ1hYWG5u7ooVK1asWLF8+fKJEyfKJzHGxcVVqVIlJiamwDfKM5Z/8Nu3b0+aNOlPf/rT5MmTv/rqK3mjtcyDtaBNATAfq9gAgCLbsWPHxx9/PHv27JCQkPfff9/f3z83N7dmzZrHjx//+9//LoT48ssvZ8+evWfPnpMnT2ZkZMyZM8fHx6ewXTNmzDDxm2thx9q0adPu3bslSZoyZcqUKVN+//vfCyEmTpz4u9/9bsGCBdu3b69WrVqNGjXmz59vHEqn04WGhj558qRKlSru7u6hoaHydhPvmjJlyunTp7/55pvY2NiwsLBff/3VYDD8+9//FkJMmDDh73//+9ChQ+fNm+fk5FRY/lOnTgUEBGRlZeX/WicHBwf5eiSdTlehQoUCLwyLi4tbtGiREOK3337r3Lmzg4ODg4ODJElNmzY9d+6cvGonhFi1apVVzIMVoU0BMJ/G9B8FAQBlyrvvviv+98w6mzFmzJhNmzZlZWWpHURl6s6DRqOJjIwMDAxU5ehmSktLq1q16p49e3x9fdXOAsDSsTYFAFCZm5tbdnZ2gbs2bNjg5+dXynkUsIGPACPWpgCYjzYFAFDZ3bt3S+EoDx8+zMnJSU9Pr1ChQokPXjofoUS80nmwDbQpAObjLhQAANv3+eef796922AwTJo0KT4+Xu04qmEezEGbAmA+1qYAALYvJCQkJCRE7RTqYx7MQZsCYD7WpgAAAJ6hTQEwH20KAADgGdoUAPPRpgAAAJ6hTQEwH20KAADgGdoUAPPRpgAAAJ6hTQEwH20KAADgGdoUAPPRpgAAAJ6hTQEwH20KAADgGdoUAPPRpgAAAJ6hTQEwH20KAADgGb1eL2hTAMxDmwIAAHgmKytLCOHk5KR2EABWgDYFAADwTGZmphDC0dFR7SAArABtCgAA4JmsrCw7OzsHBwe1gwCwAjq1AwAALMvdu3ejoqLUTgGoJisry9HRUaPRqB0EgBWgTQEA/kdCQsKgQYPUTgGoRm5TaqcAYB1oUwCAZ6Kjo9WOYGW0Wm14ePjgwYPVDoISk5mZyS0oAJiJ66YAAFBOp9PJN9SGzWBtCoD5aFMAACin1WrlL3uFzaBNATAfbQoAAOVYm7I9//nPfypVqqR2CgDWgTYFAIByrE3ZnsePH1euXFntFACsA20KAADlWJuyPWlpaVWqVFE7BQDrQJsCAEA51qZsD2tTAMxHmwIAQDnWpmwPbQqA+WhTAAAox9qU7UlLS6NNATATbQoAAOVYm7I9jx8/5ropAGaiTQEAoBxrUzbGYDA8efKEO6QDMBNtCgAA5VibsjEPHjzIy8urUaOG2kEAWAfaFAAAyrE2ZWOSk5OFEDVr1lQ7CADrQJsCAEA5nU5Hm7Il9+/fF7QpAGajTQEAoJxWq+VMP1uSnJys0+mqVq2qdhAA1oE2BQCAcqxN2Zjk5GRXV1c7O35BAmAW/rEAAEA51qZsTHJyMqf5ATAfbQoAAOVYm7Ix9+/f54Z+AMxHmwIAQDnWpmxMYmJinTp11E4BwGrQpgAAUI61KRtz69atevXqqZ0CgNWgTQEAoBxrU7ZEkqQ7d+7UrVtX7SAArAZtCgAA5VibsiX379/PzMykTQEwH20KAADlWJuyJbdv3xZC0KYAmI82BQCAcqxN2ZLbt29rtVp3d3e1gwCwGrQpAACUY23Klty+fbt27dr29vZqBwFgNWhTAAAox9qULfnXv/7VqFEjtVMAsCa0KQAAlGNtypZcvny5adOmaqcAYE1oUwAAKMfalC25evUqbQpAkdCmAABQjrUpm3H//v3U1NRmzZqpHQSANaFNAQCgHGtTNuPKlStCCNamABQJbQoAAOVYm7IZV65cqVSpUu3atdUOAsCa0KYAAFCOtSmbcfny5WbNmmk0GrWDALAmtCkAAJRjbcpmnDlzpnXr1mqnAGBlaFMAACjH2pRtyMvLO3/+fNu2bdUOAsDK6NQOAACANbl69erhw4flx0+fPj116tS9e/e++eYbIURWVtbTp09btmw5dOhQVTOiyK5cuZKRkdGuXTu1gwCwMrQpAACKQKfTjR8/XqPRaLVaIYSdnZ0kSbNmzRJCSJKUk5OzfPlytTOiyE6fPl2+fHlujw6gqDjTDwCAImjUqJF8Plhubm5ubm52dnZOTk52drb8wM7Orn///mpnRJGdOXOmVatW9vb2agcBYGVoUwAAFM37779f4J3f7OzsunbtWqNGjdKPhGI6c+YMF00BUIA2BQBA0QwaNMjOroD/QDUazZAhQ0o/D4rp6dOnZ8+e9fT0VDsIAOtDmwIAoGiqVavm5+en0xVw7XHfvn1LPw+K6cSJE0+fPu3WrZvaQQBYH9oUAABFNnLkyOdujG5nZ9elSxdO87NGhw4dcnd3r1evntpBAFgf2hQAAEXWu3fvSpUq5d/CaX7W69ChQyxMAVCGNgUAQJGVK1du2LBhz90Crl+/fmrlgWK5ubkJCQldu3ZVOwgAq0SbAgBAiaCgoNzcXPmxfDc/V1dXdSNBgVOnTmVmZtKmAChDmwIAQAkvL6/69evLjznNz3odOHCgTp06jRo1UjsIAKtEmwIAQKFRo0YZT/bjbn5WateuXX/4wx/UTgHAWtGmAABQKCgoSK/XazQaTvOzUo8ePTpx4oSfn5/aQQBYK9oUAAAK1atXz9vbW5KkoUOHqp0FSuzZs0eSpB49eqgdBIC10kiSpHYGAMArFBUVNWjQILVTAMoFBARER0e/ipFHjhx58+bNw4cPv4rBAZQFBXyPOwDA9kRGRqodwTZlZWUtXbr0k08+UTuIzQoNDX1FI0uStGfPnuDg4Fc0PoCygDYFAGVCYGCg2kezLrkAACAASURBVBFsVvfu3d3d3dVOYbNe0aqUEOLs2bNJSUlcNAWgOLhuCgCAYqFKWant27e7u7u3atVK7SAArBhtCgAAlEUxMTEDBw7UaDRqBwFgxWhTAACgzLl27dqVK1cGDhyodhAA1o02BQAAypyoqKhatWp5e3urHQSAdaNNAQCAMmfLli0DBgzQarVqBwFg3WhTAACgbLl58+b58+c5zQ9A8dGmAABA2RIdHe3i4tKlSxe1gwCwerQpAABQtoSHh7/77rs6Hd+6CaC4aFMAAKAMuXDhwsWLF4cNG6Z2EAC2gDYFAADKkPDw8Lp163bs2FHtIABsAW0KAACUFQaDYfPmzUFBQXxpL4ASQZsCAABlxcGDB+/cuTN48GC1gwCwEbQpAABQVoSHh7dt27Z58+ZqBwFgI2hTAICCPXnyRO0IFoc5sWpZWVlbtmzh/hMAShBtCgDwvBUrVnTt2rVp06bqxtDr9UeOHJk+ffru3buLM87+/fu9vLxu3bpVnEEsZE5K0NatW7t166bRaDQaTceOHTt16tS6dWsvL69p06b98ssvaqd7JaKjozMyMoYMGaJ2EAC2gzYFAHje6NGjDQZDXl6eujFOnTq1du3ar7/++u7du8UZ59GjR3fu3MnIyDBuSUpKKuogFjInz1HwQYwGDBiwceNGIUTdunWPHTsWHx9/9uzZsLCwCxcueHh4TJ8+3WAwlFxSi7B69eq+ffvWqlVL7SAAbAdtCgDwPK1W6+bmpnYK4e3tHRwcXPxxBg4cmJiYaLxUJi0tbejQoUUdxELmJD9lHyS/ihUrCiEcHR2NWzw9PWNjYwcNGvT111/Pnz+/uBEtyfXr148cOfLBBx+oHQSATaFNAQAsV7ly5Up2wIyMjMDAwGKe9WcJSuSDFHiXcDs7u6VLl9aoUWPevHm3b98uzvgWZc2aNb/73e969uypdhAANoU2BQD4rx07dowdO3batGmTJk3KfwqZJEnLly+fMGFChw4devbs+a9//UsIce7cuU8//bRBgwYZGRmjR492cXFp3779zZs35becP39+5MiRCxYs+Pjjjz/88EMT4xRVTExMcHDw1KlT/fz8ZsyYkZ2dbdx1+vTpMWPGDB06tH379itWrNDr9fL2lJSUxYsXnzhxQgixbdu2q1evpqamjhkz5ttvv1U2JyY+u+lpKYzpdxU4by9+kALnPD4+3t3dfdeuXUWZY1G5cuXAwMDMzMzIyMjCAqj1A6CMXq/fuHHj+++/r9VqS+2gAMoECQBg04y/EJsWHh7u5eWVlZUlSVJqaqqrq2utWrXkXSEhIevWrZMkSa/XN2vWrFatWhkZGUlJSW+99ZYQYuLEiZcuXTp79qyDg8PgwYPlt3h4eMTHx0uSlJmZ2blzZxPjmE71888/CyFWrVolP124cKGPj09OTo4csnHjxl26dDEYDJIk3bp1y9nZ+ddff5UkacSIEUKItm3bTpkyJT4+vnPnzkKImJgYeRB/f/969eqZM3WFzYmJz256Wgpj+l2FzdtzH6TAOY+NjXV0dAwPDy/wuGlpaUKI119//cVdmzZtEkKMHDmysACl8wMgSVJAQEBAQMBLX2batm3bNBrNjRs3ijkOADyHNgUANs6cNpWRkVG7du2IiAjjlgEDBsjNITExsWbNmnl5efL2L7/8Ugjxww8/SJL0+eefCyFSU1PlXZ06dWrcuLEkSTk5OUKIsLAwebs8rIlxTMjfppKTk52dnTdu3Gjcu3btWiHEhg0bJEmaOnWqu7u7vP369etCiBUrVshP9+zZo6BNmZgTE5/d9C4TCnuXiXnL/0EKnHOZXq8v7KAm2pR8H8UePXqo+wMglVCb8vf39/X1LeYgAPAiXSksfwEALNyRI0eSkpJatGhh3GK8YOnYsWO5ubnjxo0z7ho9erR83wL5pCmd7r//lbi5ud24cUMIYW9v37Nnz8mTJ1++fPnrr7+Wb0htYhwzJSQkZGRkuLu7G7f4+/sLIQ4ePBgUFJSYmJiZmSlvb9y4sYuLy507d+SnTk5O5h/FyMSciMI/u+ldJhT2LjPnrcA5zz9yUT1+/FgI0aRJE8v5AVDst99+27Vr1+bNm0vhWADKGtoUAEBcvXpVCGFvb//iritXrjg7O3///fdFGnDr1q1jxoxZtmzZli1boqOju3Tpomyc/OQ7Ijx8+NC4xcXFxcnJ6d69e0KIXr16bd68ed++fT169EhLS0tPT3/77bcVH0uYnJPSZP68vTjnxTyuEKJVq1aW8wOg2MqVK2vUqNGvX7/SPzQAm8ddKAAA/111KfAGbk5OTnfv3n3uG59SUlJMD2hvbx8RESF/nVHPnj2vXr2qbJz86tevL4R48Y4OHh4eQojhw4evXLlyxIgRM2fO/OSTTzZv3uzj42P+4C8yMSelyfx5e3HOFR9UkqSYmBh7e/u3337bcn4AlMnNzV27du2YMWNUL8YAbBJtCgAgWrZsKYSIjo42bjF+U22LFi0kSZo2bZpx1y+//LJ06VITo2VnZy9btkwIMXz48ISEBIPBsH//fgXjPMfLy6tixYrbt283bpHP7uvTp48Q4unTp9evXz9//vzcuXNXr15tYiHCzs4uPT39pYczMSelycS85f8gBc65MXZhg0uSVOD2v/71rxcvXpw2bVrdunUt5wdAma1btyYnJ7///vuv+kAAyibO9AMACB8fn65du65du7Zt27YjRoy4dOlSfHx8SkpKRERE3759PT09IyIinj592r9////85z9bt2794YcfxP9fWmO8EXlycrJ8+zshxOrVqz/66CP5G28rV67cunVrLy+vwsYxQT5ERkaGEMLFxSUkJCQ4OFg+nU8IsWjRoqCgoO7duwshFi9evG/fvoYNG9aqVatChQrVq1d/44035OWI5ORkIURqaqo8Zp06dVJTU8+cOZOenu7p6VnYVVUm5qRfv36FfXaNRmNi10s/6Yvv8vX1LWze8n+Qli1bvjjnQoi4uLiBAweuWbMmICCgsIMarzcTQty+ffuvf/3r4sWLJ0+e/NVXXwkhTAQohR+A4lu2bNk777xTt27dV30gAGWUOje/AACUFjPvkJ6WljZq1KiaNWu+9tprs2fPHjt27KhRo/bu3ZuXl/fgwYNhw4bVqFHD1dV1xIgRiYmJkiTt379fPvXuww8/vH///qZNm5ydnYUQs2fPzsjI8PT07N2794IFC8aOHbty5Ur5EAWOY8KJEyfka5/atm27c+dOeeO2bdt69uz50Ucfffnll99++618e3Q5T40aNezsnp1zUa9evV9//fXAgQPdunUTQrRv3z4uLk6SpPPnz7u5uTVp0iQ6OlrZnOzdu7ewzx4XF1fYLhP31jMxmXq9vrB5y/9Bnj59WuCc79+/v3bt2tu3b3/xoHv27JFv4yGE6NSpU48ePXr16uXn5/fHP/7x/Pnz+V+p1g+ArDj39Lt8+bJGo9m9e7eytwPAS2mkQlb5AQC2ISoqatCgQTb/r/3GjRsrV67s7+//4MGDlJSUxMTE8+fPp6amzp8/X+1oKJZ3331X/O8pl+YLDg7etWvX9evX89dsAChBnOkHAFCTm5tbdnZ2gbs2bNjg5+dnziBnzpz57LPPEhMThRCurq6urq7NmjVr27ZtREREKRzdTKV5LAgh0tPTN23a9MUXX1ClALw6tCkAgJqeu8mbMj///PO9e/fmzp3bu3fvpk2bPnnyJCEhIS4ubsGCBaVwdDOV5rEghNiwYUNOTg73nwDwSvHXGgCA1Rs+fPjMmTOXLFnStm3bGjVqvPPOOw8fPvzuu+9K58thYYEkSQoLCwsKCqpevbraWQDYMtamAABWT6vVzpkzZ86cOZmZmY6Ojqbvnoey4B//+MfVq1ejoqLUDgLAxtGmAAC2o7DbnaOs+e6773x9fVu0aKF2EAA2jjYFAABsyvXr1/fs2fPTTz+pHQSA7eO6KQAAYFNCQ0MbNmzIbRIBlALaFAAAsB1paWkbN26cNGkSN0YHUAr4hwYAANiO77//3s7O7r333lM7CIAygTYFAABsRF5e3rJly0aPHl2pUiW1swAoE2hTAADARmzbtu327dsTJ05UOwiAsoI2BQAAbMR33333zjvvNGzYUO0gAMoK7pAOAABswdmzZ+Pj4/fv3692EABlCGtTAADAFvztb3974403unXrpnYQAGUIa1MAAMDq3b9/PyoqasmSJRqNRu0sAMoQ1qYAAIDVW7p0aYUKFYYMGaJ2EABlC20KAABYt5ycnBUrVowbN87R0VHtLADKFs70A4AygdOfYL0CAgJMv+CHH3548ODB+PHjSycPABhpJElSOwMA4BW6e/fusWPH1E6BAuzcuXPTpk1ffPHFG2+8oXYWi+bu7u7t7V3YXkmSWrdu3axZs4iIiNJMBQCCNgUAgIoGDx4cFxd36tSpBg0aqJ3FWu3atatXr15nzpxp06aN2lkAlDm0KQAAVJOVldWpU6e8vLxjx445OTmpHccqdevWzcnJaefOnWoHAVAWcRcKAABU4+jouGXLlrt3744dO1btLFbp5MmThw4dmjZtmtpBAJRRrE0BAKCyuLg4Pz+/0NDQ4OBgtbNYmX79+iUlJZ04cULtIADKKNamAABQma+v7+zZs//4xz8eOnRI7SzW5Ny5cz/++OP06dPVDgKg7GJtCgAA9UmSFBgYeOTIkdOnT7u5uakdxzr06dPn3r17p06d4gsAAKiFNgUAgEVIT0/38vKqUKHCoUOHHBwc1I5j6U6fPt2+ffvY2Fg/Pz+1swAou2hTAABYiuvXr7dv375v377r169XO4ul+8Mf/pCenn706FG1gwAo03RqBwAAAP/VpEmTDRs29O/f38fHh7v8mXDkyJE9e/bs27dP7SAAyjrWpgAAsCxffvnl/Pnz9+/f36lTJ7WzWCJJknx8fBwdHWlTAFRHmwIAwLIYDIY+ffqcOXPmzJkzderUUTuOxYmKihoyZMjJkyfbtm2rdhYAZR1tCgAAi5OWlubp6enq6nrw4MFy5cqpHceC5OTkNG/evGPHjlxaBsAS8H1TAABYnCpVqvz000+XLl365JNP1M5iWcLCwhITE+fOnat2EAAQgjYFAIBlev3119etW7dkyZI1a9aoncVSPHjwYN68eR9//PFrr72mdhYAEIIz/QAAsGTTpk1btGjR4cOHPT091c6ivg8//HDbtm3Xrl2rVKmS2lkAQAjaFAAAlsxgMPTu3fvy5cunT592dXVVO46azp496+npuWbNmhEjRqidBQD+izYFAIBFe/jwoaenZ926dffs2aPTldEvipQkqVu3brm5uUePHtVoNGrHAYD/4ropAAAsWrVq1bZu3XrixInPPvtM7Syq2bhxY3x8/HfffUeVAmBRWJsCAMAKREREDBs2bN26de+9957aWUrb48ePmzZt2qdPn+XLl6udBQD+B20KAADrMGXKlJUrV8bHx7dp00btLKVq/Pjx27Ztu3z5cvXq1dXOAgD/gzYFAIB10Ov1vr6+t27dOnXqlIuLi9pxSsmhQ4fefPPNzZs3Dxo0SO0sAPA82hQAAFbj/v37bdu2ff311//xj39otVq147xy2dnZrVu3rl+/fmxsrNpZAKAA3IUCAACrUaNGjR9//PHo0aNffvml2llKw6xZsxITE7lcCoDFok0BAGBNWrduvWLFipCQkOjoaLWzvFrnz59fuHDhN9984+7urnYWACgYZ/oBAGB9JkyYsGnTpoSEhObNm6ud5ZXQ6/VeXl7lypWLj4+3s+OPvwAsFG0KAADrk5ub26NHj+Tk5JMnT1auXFntOCXvm2++mTVr1tmzZ5s2bap2FgAoFH/sAQDA+tjb20dFRWVkZAQFBRkMBrXjlLB//etfX3311axZs6hSACwca1MAAFir48ePd+vWbfr06bZ0UwpJkrp37/7o0aNTp07Z29urHQcATNGpHQAAACjk7e0dGhoaHBzcpk0bf39/teOUjEWLFsXHxyckJFClAFg+1qYAALBuo0ePjoqKOnHihA2cF3f58uV27dp99tlntrTaBsCG0aYAALBuT58+7dKly5MnT06cOFGpUiW14yiXnZ3doUMHZ2fnQ4cO6XScPgPACnAXCgAArFv58uW3bNny8OHD9957z6r/SDp9+vQbN26sW7eOKgXAWtCmAACweu7u7lu2bImNjV2wYIHaWRQ6fPhwaGhoWFhY48aN1c4CAOaiTQEAYAs6der0zTfffPHFF7t27cq/Xa/XHz58WK1UBcrJyTl58mT+LWlpaUFBQX379h01apRaqQBAAdoUAAA24uOPPx4xYsSwYcN++eUXeUtKSsqbb745dOhQizoDcO/evZ06dQoLCzNumTBhgl6v//7771VMBQAK0KYAALAdS5cubdCgwYABAzIzM0+fPt2qVauEhITExMSjR4+qHe2ZmJiYvLy8yZMnv/vuu0+ePNmwYUNkZOSqVauqV6+udjQAKBru6QcAgE359ddf27Vr165du4MHDxoMBr1eb29vP2bMmCVLlqgdTQghcnNzq1ev/uTJEyGEvb19nTp1Hjx4MHr06NDQULWjAUCRsTYFAIBNcXd39/Pz27NnT25url6vF0Lk5uZGRETIj1W3f/9+uUoJIXJzcxMTE58+ferh4aFuKgBQhjYFAIDtkC+U+uGHH4QQ+U8/SUtL27dvn3q5nomJibG3tzc+1ev1er1+woQJw4cPz8rKUjEYAChAmwIAwEYcP368efPmJ06cyMvLe26Xvb19RESEKqnyy8vLi4mJyc3NfXFXZGSkl5fXr7/+WvqpAEAx2hQAALYgPT19zJgxKSkpBXaV3NzcmJiYp0+fln6w/A4dOpSWllbgLkmSLl68uGbNmlKOBADFQZsCAMAWVKhQ4fTp07NmzbK3t9fpdC++ICsra+fOnaUfLL+YmJhy5cq9uF2n09WpU2ffvn1z584t/VQAoBhtCgAAG1G+fPnZs2dfunTJ29vbzu75/+K1Wu2mTZtUCSYzGAzR0dE5OTn5N+p0Oo1GM2rUqCtXrrz55ptqZQMAZbhDOgAAtkaSpI0bNwYHB2dlZeU/8c/e3j4lJaVy5cqqpDp06FC3bt3yb9FqtQ0bNtywYUOHDh1UiQQAxcTaFAAAtkaj0YwYMeLatWuBgYFCCOM6VV5e3o8//qhWqi1bthhP89PpdHZ2dlOnTr1w4QJVCoD1Ym0KAABbFhsbO3bs2Pv37+v1eq1W26NHj927d5d+DEmSateunZycLITQarVNmjTZsGFDu3btSj8JAJQg1qYAALBlvXv3vnbt2kcffWRnZ2cwGPbt25eamlr6MRISEpKTk+3s7HQ63VdffXX+/HmqFAAbwNoUAACvxPHjxxcuXKh2imcePXp0+vTpx48ft2nTpkGDBqV89AsXLly/fr1q1aqenp6VKlUq5aMX6I9//KO3t7faKQBYN9amAAB4Je7cuRMTE6N2imeqVq361ltvtWzZ8t69e6V/9H//+98tW7bs3r27hVSpmJiYO3fuqJ0CgNUr4PsoAABASYmOjlY7wvNu377t7u7+4i3UX51Hjx49ePCgUaNGpXbEl9JoNGpHAGALaFMAAJQtdevWLeUjVq1atWrVqqV8UAAoBZzpBwAAAABK0KYAAAAAQAnaFAAAAAAoQZsCAAAAACVoUwAAAACgBG0KAAAAAJSgTQEAAACAErQpAAAAAFCCNgUAAAAAStCmAAAAAEAJ2hQAAAAAKEGbAgAAAAAlaFMAAFiHJ0+eKNhlk5gKABaCNgUAgKVbsWJF165dmzZtWqRd+en1+iNHjkyfPn337t3FSbJ//34vL69bt24VZxATevfunZKSYuIFxZ8KAChBtCkAACzd6NGjDQZDXl5ekXbld+rUqbVr13799dd3794tTpJHjx7duXMnIyPDuCUpKak4A+Z37dq1nTt3fv/99yZeU/ypAIASRJsCAMDSabVaNze3ou7Kz9vbOzg4uPhJBg4cmJiY2Lx5c/lpWlra0KFDiz+sbPny5eXLl1+2bJlery/sNcWfCgAoQbQpAADKhHLlypXsgBkZGYGBgSV11l9WVtbx48enTp169+7d7du3l8iYAPCq6dQOAABAmZacnDxjxozXXnvtt99+S01NXbVqVfXq1eVdO3bsiI2NrVq1alZW1nMn1JnYZb6YmJhDhw45ODhcunSpbdu2M2fOdHBwkHedPn16xYoVGRkZN27c+OCDDz744AOdTieESElJiYyM9PT07NChw7Zt265evfro0aMxY8Z4eHhMnTr1/PnzoaGhzZo1S0pKys7OXrp0qRAiPj5+yJAhK1eu9PPzMxFm8+bNgYGBgYGBISEhYWFhAQEBZn7eEpkKAFBIAgAAr0BkZKQ5/89269Zt0KBB8uNWrVoNHz5cfhweHu7l5ZWVlSVJUmpqqqura61atV66y4Sff/5ZCLFq1Sr56cKFC318fHJycuRBGjdu3KVLF4PBIEnSrVu3nJ2df/31V0mSRowYIYRo27btlClT4uPjO3fuLISIiYmRB/H3969Xr57xEB4eHvHx8ZIkZWZmdu7cWd4YGxvr6OgYHh5uOl6nTp1SU1MlSerXr58Q4ty5c8ZdJT4VkiQJISIjI815JQCYwJl+AACorFWrVvKDN95448KFC0KIzMzMqVOnTpo0qXz58kKI6tWryzXG9C7z3b9/f+bMmePHj7e3t5cH+eKLLw4fPrxp0yYhxOLFi6tVq1avXj0hxIwZM4QQY8eODQ0N9fHxmTlzZmFj5ubmXrt27ezZs0IIR0fHCRMmyNt79er15MkT05dXnT59un79+vKinPzGxYsXv/TzlshUAEBx0KYAAFDTgQMHPv/886ysrFWrVp08eTIzM1MIceTIkaSkpBYtWhhfZrzqycQu8yUkJGRkZLi7uxu3+Pv7CyEOHjwohEhMTJRjCCEaN27s4uJy584d+amTk1NhY9rb2/fs2XPy5MkffvhhWlrakCFDjLu0Wq3pPEuXLh03bpz82NfXt1GjRuHh4Q8fPhSvfioAoDhoUwAAqCkvLy8kJOS9995r0qRJhw4d5I1Xr14VQsgLR88xsct8t2/fFkLIdUXm4uLi5OR07949IUSvXr0ePHiwb98+IURaWlp6evrbb79tzrBbt24dNGjQsmXLPDw8Dh8+bGaYtLS0uLi4qVOnent7e3t7d+zYUQiRlZW1evVq8eqnAgCKgzYFAIBqDAZDr169Ll68GBUV1aVLF+N2eY1F7jzPMbHLfPXr1xdC3Lx587ntHh4eQojhw4evXLlyxIgRM2fO/OSTTzZv3uzj42POsPb29hERERs3bhRC9OzZU247L7V69epPP/30eD779+/XarVLly41GAyveioAoDhoUwAAqObkyZN79uzp0aOH/DQ3N1eSJCFEy5YthRDR0dHGVxq/l9bELvN5eXlVrFgx/43I5bP7+vTpI4R4+vTp9evXz58/P3fu3NWrV8u3hSiQnZ1denq6/Dg7O3vZsmVCiOHDhyckJBgMhv379xsTFjZCTk7O4sWLn7uqyt3d/e23375169ZPP/30qqcCAIqDNgUAgGo0Go0QYv369RcvXly3bt3ly5eTk5MvXLjQqFGjrl27rl27dvny5ZmZmadOnYqPj09JSYmIiGjdunVhu4wXOxXo8ePHQoiMjAwhhIuLS0hIyNGjR+XT+YQQixYtCgoK6t69uxBi8eLF+/bti4mJ2b59+969e8+ePZubmyu/LDk5WQiRmpoqP61Tp05qauqZM2cOHTqUmZm5evVqucy4ublVrly5devWQoi4uLgqVarExMQUmGr9+vW1a9d2cXF5brt8HdecOXM6duxY4lMBACWFNgUAgGo6dOgwfvz4c+fOjR071t3dff78+eXLl587d26FChV27NgxYsSI2bNnN23adOfOnf7+/qNGjapZs2b58uVN7CrsQCdPnpw7d64QYsOGDbt27RJCTJw4cevWrQsWLAgODp41a1aNGjXWr18vv7ht27aJiYkTJ07s37+/r69vmzZtmjRpcuvWrYMHD4aFhQkh1qxZs3fvXiHEhAkT3Nzchg4dmpKS4uTkVL58+b59+/7lL3/56KOPvv76a29vbyGETqerUKFCgVc3bdu27Ysvvvj555+Nd/CTHT9+fOfOnUKIf/7znyNGjFi/fn0JTgUAlCCNfEYBAAAoWVFRUfIXSakdpMg2btxYuXJlf3//Bw8epKSkJCYmnj9/PjU1df78+WpHKzEajSYyMjIwMFDtIACsm07tAAAAoGS4ubllZ2cXuGvDhg1+fn7mDHLmzJnPPvssMTFRCOHq6urq6tqsWbO2bdtGRESUZFYAsAm0KQAAbMTdu3eLP8jPP/987969uXPn9u7du2nTpk+ePElISIiLi1uwYEHxBwcAG8N1UwAA4Jnhw4fPnDlzyZIlbdu2rVGjxjvvvPPw4cPvvvvO0dFR7WgAYHFYmwIAAM9otdo5c+bMmTMnMzPT0dFRvusgAKBAtCkAAFAAJycntSMAgKXjTD8AAAAAUII2BQAAAABK0KYAAAAAQAnaFAAAAAAoQZsCAAAAACVoUwAAAACgBG0KAAAAAJSgTQEAAACAErQpAAAAAFCCNgUAAAAAStCmAAAAAEAJ2hQAAAAAKKFTOwAAALbs3XffVTsCAOBVYW0KAIBXwt3dPSAgQO0UJeb06dOnT59WO0WJCQgIcHd3VzsFAKunkSRJ7QwAAMDSBQYGCiGioqLUDgIAFoS1KQAAAABQgjYFAAAAAErQpgAAAABACdoUAAAAAChBmwIAAAAAJWhTAAAAAKAEbQoAAAAAlKBNAQAAAIAStCkAAAAAUII2BQAAAABK0KYAAAAAQAnaFAAAAAAoQZsCAAAAACVoUwAAAACgBG0KAAAAAJSgTQEAAACAErQpAAAAAFCCNgUAAAAAStCmAAAAAEAJ2hQAcyJGsgAADCBJREFUAAAAKEGbAgAAAAAlaFMAAAAAoARtCgAAAACUoE0BAAAAgBK0KQAAAABQgjYFAAAAAErQpgAAAABACdoUAAAAAChBmwIAAAAAJWhTAAAAAKAEbQoAAAAAlKBNAQAAAIAStCkAAAAAUEIjSZLaGQAAgMVZt27d3/72t7y8PPlpSkqKEMLV1VV+qtVqp0yZMnLkSLXiAYAloE0BAIACXLt27fXXXzfxgitXrph+AQDYPM70AwAABfDw8GjRooVGo3lxl0ajadGiBVUKAGhTAACgYCNGjNBqtS9u1+l07733XunnAQBLw5l+AACgYPfu3XNzc3vxVwWNRvPbb7+5ubmpkgoALAdrUwAAoGB16tTp2LGjnd3//LZgZ2fXsWNHqhQACNoUAAAwISgo6LlLpzQazYgRI9TKAwAWhTP9AABAoR4+fFizZk29Xm/cotVqk5OTq1evrmIqALAQrE0BAIBCVatWzdfXV6fTyU+1Wq2vry9VCgBktCkAAGDK8OHDDQaD/FiSpKCgIHXzAIDl4Ew/AABgSkZGhouLy9OnT4UQDg4OqampFSpUUDsUAFgE1qYAAIApzs7Offr0sbe31+l0/fr1o0oBgBFtCgAAvMSwYcP0en1eXt7QoUPVzgIAFkSndgAAAKzP3bt3jx07pnaK0pOXl1e+fHlJktLT06OiotSOU3r4Zi0ApnHdFAAARRYVFTVo0CC1U+CVi4yMDAwMVDsFAMvF2hQAAAqVqb9IHjhwQKPRdOvWTe0gpee5ry0GgBfRpgAAwMt17dpV7QgAYHFoUwAA4OXs7LhzFQA8j38ZAQAAAEAJ2hQAAAAAKEGbAgAAAAAlaFMAAAAAoARtCgAAAACUoE0BAAAAgBK0KQAAAABQgjYFAAAAAErQpgAAAABACdoUAAAAAChBmwIAAAAAJWhTAABYIkmSzp07l52drXYQAEChaFMAAFiciIiIhg0btm7dOi0tTe0sJW/r1q3dunXTaDQajaZjx46dOnVq3bq1l5fXtGnTfvnlF7XTAUAR0KYAAHiFkpKSFLxr6NChAQEBJR7GBGU5lQ0yYMCAjRs3CiHq1q177Nix+Pj4s2fPhoWFXbhwwcPDY/r06QaDofhhAKAU0KYAAHhV0tLShg4dquy91atXL9kwJhQnp7JBKlasKIRwdHQ0bvH09IyN/b/27jQkqv4L4PixcSotKcrCYqIFHgtJrBfmRAvRMqRtWKJFGhYzlUuWUUYrSdmEVEQvLIqgRQvNNkqifcFCijKTrGyhKHNJJckZcZv7f3H/zCPPpI7jYz7R9/Pqzr2/e+65v1dzuL97bk5ERMSePXv27t3byWQA4NegmgIAoEtYLJbw8PCPHz92dyLt+Ffy7GgQNzc3x509evRIS0sbPHhwSkrKp0+fOpMPAPwaVFMAAHSJixcvvn79urKy0mQy7du3T0TKy8tNJtOuXbtMJlNoaGhVVZU6sqCgIDo6OjU1NTExMTY21jHU1atXNRpNRETE5cuX271udnb2mjVrNmzYEBwcvG3bNrWPRWZmppeX17Bhw0SkpqZm165dGo1m4sSJjnkWFRVt3brVz8+vpKRkwYIFAwYMmDBhQl5eXoeCiEhubu6wYcOuXbvWoUnr169feHi41WrNzMwUEUVRjhw5EhMTExQUZDAY3r59KyLPnz/fuHHjqFGjLBaL0Wj09vaeMGHChw8f2pjMn8YBgH+BAgAAOsj+X79tc+fOHTFihP3ntGnTIiIi1O2AgIDIyEh1e/To0bm5uYqiWK3WKVOmqDvV1W5lZWWKoiQlJR04cMCZxA4cODBp0qSGhgZFUSorK//666+pU6fabDZFUQwGg06ns4/09/fX6/WOeT548MDPz0+j0axbt+7u3bvnz58fOHCgp6fn169fnQ+iKEpOTo6Hh0dGRsZP81S7a4wZM8bxUHp6uohER0crimI2m0+cOKEoSlNTk5+fn4+Pj8ViKS0tnTlzpojExcW9fPkyPz+/V69eixcvbmMyfxqn3ckUkczMzHaHAfiT8WwKAIBfJyAgQN0YO3bsixcvRKSxsfHNmzf5+fki4uHhERMT03K8zWbbvHmzXq9PTExsN3hFRcX27dtXr16t1WpFZODAgVu2bHnw4IFan3h6erYc3KdPn58GmTJlSlBQkJubW2pq6rRp0xYuXJiWlma1Wo8cOeJ8EBEJCQn58eOHC69jDRo0SEQ+f/789evXgwcPRkVFiYhGowkLCysrK7ty5YqPj09gYKCIJCcn+/n5jRs3LjAw8OnTp9LKZLYWp6OJAYAj9+5OAACAP8Xdu3dFpK6uLiMj4/Hjx4qiiIhWqzUYDGvXri0qKtqzZ8+SJUtanhIXF+fj4xMaGupM/Ly8PIvFoq7EU82dO1dE7t27p9YSTtJoNO7u7mpJJiKhoaE9e/YsLCx0PoI9TkdPEZGamhoR8fX1ffToUWNj46pVq+yHjEaj2rhCjezu/v+/MTqd7t27d9LKZLYRBwA6iWoKAIBfpLm5OTU1NT8/Pz4+PigoSH0ZSUQuXLhgMpkOHz58/vz5c+fOTZ061X6Kp6fnsWPHoqKi1NeT2qZ2bqiurrbv8fb2VhfpdSZtrVY7dOjQpqamzgRx3qtXr0QkICDg1atXffr0OXbsWIdOd5xM1+IAgDNY6QcAwK9gs9lCQkIKCwuzsrJa1ksiotVqz5w5o36CyWAwvH792n4oJSVlzJgxS5YsceYzviNHjhQRez8Gu9GjR3cy+YaGhs4HcYaiKNnZ2Vqtdvbs2Z6enl++fPny5UvLAd++fWs7guNkuhYHAJxBNQUAQFfp0aNHbW2tuv348eMbN27MmDFD/dnY2Kiu9Kuvrz98+LCIREZG5uXl2Wy2O3fu2CP07t379OnTpaWlJpOp3cvp9XovL69Lly7Z95SUlFit1vnz54uIu7t7bW1tc3Ozeqi2ttb+kdyWeTqqqKgoKytbtGhRR4O08RFe9d4d7d+/v7CwcNOmTcOHD/f391cUZdOmTfaj79+/T0tLa/X+W5lMF+IAgJOopgAA6CpDhw6trKx8+vTp/fv36+rqROTkyZOFhYUnTpwoKioqLy9/8eJFeXn58ePH1fpEp9P169dv/PjxImKxWESkqalp3LhxycnJ2dnZZrO57ct5e3ubzeaHDx/evn1b3XPo0KGoqKjp06eLiL+///fv381mc3Fx8e7du+vr64uLi589e/aPPK1Wq4jU19fbX5RKSUmJjIzU6/UdCnLz5s3+/ftnZ2f/NFX15Sj1WqpPnz4lJCQkJSWtXbs2OTlZRGbNmhUYGHjmzJlFixalp6enpaWtWrUqLi7Ofrp98WF5eXldXZ1aoTlOZhtxAKCzurGfIAAAvyknO6QXFBTodDpfX99z584pirJ69WovLy+9Xn/r1q2cnBxvb++wsLCqqqrAwMA5c+akpqauXLny6NGjiqKcPXvW19dXRGJjY4uLi/Py8tS+C0aj8e3bt21f9OLFiwaDIT4+fseOHfv27VPboyuKUlNTM2/evL59++r1+idPnqxYsSI6OjonJ8cxT6PRqNVqly9fHhYWZjQak5OTm5ubOxrkzp07Q4YMuXTpkmOGN27cUNtjiMjkyZNnzJgREhISHBy8fv36goKCliOrqqqWLl06ePDgQYMGLVu2rKSkRI2srmmMjY2tqKhIT09XWwvu3LnTYrE4TmZrcdoldEgH0B43pZVH7QAAoDVZWVnql6O6O5EuYTKZ0tPT1YdpfzI3N7fMzMzw8PDuTgTAfxc9/QAA+J3odLr6+vqfHjp16lRwcPAvzgcA/mRUUwAA/E7+0ZuuK1RXVzc0NNTW1vbt27errwUAvzW6UAAAgL9t3rz5+vXrNpstISEhNze3u9MBgP80nk0BAIC/mc3mdpsHAgBUPJsCAAAAAFdQTQEAAACAK6imAAAAAMAVVFMAAAAA4AqqKQAAAABwBdUUAAAAALiCagoAAAAAXEE1BQAAAACuoJoCAAAAAFdQTQEAAACAK6imAAAAAMAVVFMAAAAA4AqqKQAAAABwhXt3JwAAwO8qKyuru1MAAHQnqikAAFwUERHR3SkAALqTm6Io3Z0DAAAAAPx+eG8KAAAAAFxBNQUAAAAArqCaAgAAAABXUE0BAAAAgCv+B0Y/RSqJbH9sAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_model(dt.get_model().model,rankdir='TB')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear+DNN+AutoInt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start cross validation\n",
      "2 class detected, {0, 1}, so inferred as a [binary classification] task\n",
      "Preparing features cost:0.20935845375061035\n",
      "Imputation cost:6.592493295669556\n",
      "Categorical encoding cost:16.10503888130188\n",
      "fit_transform cost:23.096850633621216\n",
      "transform X_test\n",
      "transform_X cost:183.24825382232666\n",
      "Iterators:KFold(n_splits=3, random_state=9527, shuffle=True)\n",
      "Injected a callback [EarlyStopping]. monitor:val_AUC, patience:1, mode:max\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Fold:1\n",
      "\n",
      "1 Physical GPUs, 1 Logical GPUs\n",
      ">>>>>>>>>>>>>>>>>>>>>> Model Desc <<<<<<<<<<<<<<<<<<<<<<< \n",
      "---------------------------------------------------------\n",
      "inputs:\n",
      "---------------------------------------------------------\n",
      "['all_categorical_vars: (23)', 'input_continuous_all: (5)']\n",
      "---------------------------------------------------------\n",
      "embeddings:\n",
      "---------------------------------------------------------\n",
      "input_dims: [4, 4, 4, 4, 4, 5, 8, 8, 8, 6, 1222, 1521, 224, 224, 2220, 5, 7, 8, 17, 28, 192, 9, 14]\n",
      "output_dims: [20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
      "dropout: 0.3\n",
      "---------------------------------------------------------\n",
      "dense: dropout: 0\n",
      "batch_normalization: False\n",
      "---------------------------------------------------------\n",
      "concat_embed_dense: shape: (None, 465)\n",
      "---------------------------------------------------------\n",
      "nets: ['linear', 'dnn_nets', 'autoint_nets']\n",
      "---------------------------------------------------------\n",
      "linear: input_shape (None, 28), output_shape (None, 1)\n",
      "dnn: input_shape (None, 465), output_shape (None, 300)\n",
      "autoint: input_shape (None, 23, 20), output_shape (None, 460)\n",
      "---------------------------------------------------------\n",
      "stacking_op: add\n",
      "---------------------------------------------------------\n",
      "output: activation: sigmoid, output_shape: (None, 1), use_bias: False\n",
      "loss: binary_crossentropy\n",
      "optimizer: Adam\n",
      "---------------------------------------------------------\n",
      "\n",
      "Train on 400000 samples, validate on 200000 samples\n",
      "400000/400000 [==============================] - 67s 168us/sample - loss: 0.4379 - AUC: 0.7370 - accuracy: 0.8128 - val_loss: 0.4035 - val_AUC: 0.7815 - val_accuracy: 0.8228\n",
      "Fold 1 fitting over.\n",
      "Fold 1 scoring over.\n",
      "Save model to:dt_output/dt_20200324 155806_linear_dnn_nets_autoint_nets/linear_dnn_nets_autoint_nets-kfold-1.h5.\n",
      "\n",
      "Fold:2\n",
      "\n",
      "1 Physical GPUs, 1 Logical GPUs\n",
      ">>>>>>>>>>>>>>>>>>>>>> Model Desc <<<<<<<<<<<<<<<<<<<<<<< \n",
      "---------------------------------------------------------\n",
      "inputs:\n",
      "---------------------------------------------------------\n",
      "['all_categorical_vars: (23)', 'input_continuous_all: (5)']\n",
      "---------------------------------------------------------\n",
      "embeddings:\n",
      "---------------------------------------------------------\n",
      "input_dims: [4, 4, 4, 4, 4, 5, 8, 8, 8, 6, 1222, 1521, 224, 224, 2220, 5, 7, 8, 17, 28, 192, 9, 14]\n",
      "output_dims: [20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
      "dropout: 0.3\n",
      "---------------------------------------------------------\n",
      "dense: dropout: 0\n",
      "batch_normalization: False\n",
      "---------------------------------------------------------\n",
      "concat_embed_dense: shape: (None, 465)\n",
      "---------------------------------------------------------\n",
      "nets: ['linear', 'dnn_nets', 'autoint_nets']\n",
      "---------------------------------------------------------\n",
      "linear: input_shape (None, 28), output_shape (None, 1)\n",
      "dnn: input_shape (None, 465), output_shape (None, 300)\n",
      "autoint: input_shape (None, 23, 20), output_shape (None, 460)\n",
      "---------------------------------------------------------\n",
      "stacking_op: add\n",
      "---------------------------------------------------------\n",
      "output: activation: sigmoid, output_shape: (None, 1), use_bias: False\n",
      "loss: binary_crossentropy\n",
      "optimizer: Adam\n",
      "---------------------------------------------------------\n",
      "\n",
      "Train on 400000 samples, validate on 200000 samples\n",
      "400000/400000 [==============================] - 71s 177us/sample - loss: 0.4594 - AUC: 0.7351 - accuracy: 0.8116 - val_loss: 0.3992 - val_AUC: 0.7815 - val_accuracy: 0.8253\n",
      "Fold 2 fitting over.\n",
      "Fold 2 scoring over.\n",
      "Save model to:dt_output/dt_20200324 155806_linear_dnn_nets_autoint_nets/linear_dnn_nets_autoint_nets-kfold-2.h5.\n",
      "\n",
      "Fold:3\n",
      "\n",
      "1 Physical GPUs, 1 Logical GPUs\n",
      ">>>>>>>>>>>>>>>>>>>>>> Model Desc <<<<<<<<<<<<<<<<<<<<<<< \n",
      "---------------------------------------------------------\n",
      "inputs:\n",
      "---------------------------------------------------------\n",
      "['all_categorical_vars: (23)', 'input_continuous_all: (5)']\n",
      "---------------------------------------------------------\n",
      "embeddings:\n",
      "---------------------------------------------------------\n",
      "input_dims: [4, 4, 4, 4, 4, 5, 8, 8, 8, 6, 1222, 1521, 224, 224, 2220, 5, 7, 8, 17, 28, 192, 9, 14]\n",
      "output_dims: [20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
      "dropout: 0.3\n",
      "---------------------------------------------------------\n",
      "dense: dropout: 0\n",
      "batch_normalization: False\n",
      "---------------------------------------------------------\n",
      "concat_embed_dense: shape: (None, 465)\n",
      "---------------------------------------------------------\n",
      "nets: ['linear', 'dnn_nets', 'autoint_nets']\n",
      "---------------------------------------------------------\n",
      "linear: input_shape (None, 28), output_shape (None, 1)\n",
      "dnn: input_shape (None, 465), output_shape (None, 300)\n",
      "autoint: input_shape (None, 23, 20), output_shape (None, 460)\n",
      "---------------------------------------------------------\n",
      "stacking_op: add\n",
      "---------------------------------------------------------\n",
      "output: activation: sigmoid, output_shape: (None, 1), use_bias: False\n",
      "loss: binary_crossentropy\n",
      "optimizer: Adam\n",
      "---------------------------------------------------------\n",
      "\n",
      "Train on 400000 samples, validate on 200000 samples\n",
      "400000/400000 [==============================] - 69s 174us/sample - loss: 0.7155 - AUC: 0.6787 - accuracy: 0.7895 - val_loss: 0.4061 - val_AUC: 0.7745 - val_accuracy: 0.8207\n",
      "Fold 3 fitting over.\n",
      "Fold 3 scoring over.\n",
      "Save model to:dt_output/dt_20200324 155806_linear_dnn_nets_autoint_nets/linear_dnn_nets_autoint_nets-kfold-3.h5.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:  5.0min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fit_cross_validation cost:507.61229848861694\n",
      "CPU times: user 17min 54s, sys: 2min 15s, total: 20min 9s\n",
      "Wall time: 8min 27s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "conf = ModelConfig(\n",
    "    dnn_params={\n",
    "        'hidden_units':((300, 0.3, True),(300, 0.3, True),), #hidden_units\n",
    "        'dnn_activation':'relu',\n",
    "    },\n",
    "    fixed_embedding_dim=True,\n",
    "    embeddings_output_dim=20,\n",
    "    nets =['linear','autoint_nets','dnn_nets'],\n",
    "    output_use_bias = False,\n",
    "    autoint_params={\n",
    "        'num_attention': 3,\n",
    "        'num_heads': 1,\n",
    "        'dropout_rate': 0,\n",
    "        'use_residual': True,\n",
    "    },\n",
    ")\n",
    "\n",
    "dt = DeepTable(config = conf)\n",
    "oof_proba, eval_proba, test_prob = dt.fit_cross_validation(\n",
    "    X, y, X_eval=None, X_test=X_test, \n",
    "    num_folds=n_folds, stratified=False, iterators=None,\n",
    "    batch_size=batch_size, epochs=epochs, verbose=1, callbacks=[], n_jobs=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Output submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission['target'] = test_prob\n",
    "submission.to_csv(f'submission_linear_dnn_autoint_kfold50.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load model from disk:dt_output/dt_20200324 155806_linear_dnn_nets_autoint_nets/linear_dnn_nets_autoint_nets-kfold-3.h5.\n",
      "1 Physical GPUs, 1 Logical GPUs\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_model(dt.get_model().model,rankdir='TB')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FM+DCN+DNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start cross validation\n",
      "2 class detected, {0, 1}, so inferred as a [binary classification] task\n",
      "Preparing features cost:0.01894521713256836\n",
      "Imputation cost:0.04121232032775879\n",
      "Categorical encoding cost:0.05318951606750488\n",
      "fit_transform cost:0.1182854175567627\n",
      "Iterators:KFold(n_splits=3, random_state=9527, shuffle=True)\n",
      "Injected a callback [EarlyStopping]. monitor:val_AUC, patience:1, mode:max\n",
      "\n",
      "Fold:1\n",
      "\n",
      "1 Physical GPUs, 1 Logical GPUs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>>>>>>>>>>>>>>>>>>>>> Model Desc <<<<<<<<<<<<<<<<<<<<<<< \n",
      "---------------------------------------------------------\n",
      "inputs:\n",
      "---------------------------------------------------------\n",
      "['all_categorical_vars: (23)', 'input_continuous_all: (5)']\n",
      "---------------------------------------------------------\n",
      "embeddings:\n",
      "---------------------------------------------------------\n",
      "input_dims: [4, 4, 4, 4, 4, 5, 8, 8, 8, 6, 592, 669, 206, 194, 748, 5, 7, 8, 17, 28, 174, 9, 14]\n",
      "output_dims: [20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
      "dropout: 0.3\n",
      "---------------------------------------------------------\n",
      "dense: dropout: 0\n",
      "batch_normalization: False\n",
      "---------------------------------------------------------\n",
      "concat_embed_dense: shape: (None, 465)\n",
      "---------------------------------------------------------\n",
      "nets: ['dcn_nets', 'dnn_nets', 'fm_nets']\n",
      "---------------------------------------------------------\n",
      "dcn-widecross: input_shape (None, 465), output_shape (None, 465)\n",
      "dcn-dnn2: input_shape (None, 465), output_shape (None, 300)\n",
      "dcn: input_shape (None, 465), output_shape (None, 765)\n",
      "dnn: input_shape (None, 465), output_shape (None, 300)\n",
      "fm: input_shape (None, 23, 20), output_shape (None, 1)\n",
      "---------------------------------------------------------\n",
      "stacking_op: add\n",
      "---------------------------------------------------------\n",
      "output: activation: sigmoid, output_shape: (None, 1), use_bias: False\n",
      "loss: binary_crossentropy\n",
      "optimizer: Adam\n",
      "---------------------------------------------------------\n",
      "\n",
      "Train on 666 samples, validate on 334 samples\n",
      "Epoch 1/100\n",
      "666/666 [==============================] - 9s 14ms/sample - loss: 1.1476 - AUC: 0.5170 - accuracy: 0.5240 - val_loss: 0.6022 - val_AUC: 0.6484 - val_accuracy: 0.7186\n",
      "Epoch 2/100\n",
      "666/666 [==============================] - 0s 240us/sample - loss: 0.6107 - AUC: 0.6279 - accuracy: 0.7658 - val_loss: 0.4847 - val_AUC: 0.7210 - val_accuracy: 0.7994\n",
      "Epoch 3/100\n",
      "666/666 [==============================] - 0s 241us/sample - loss: 0.4738 - AUC: 0.7726 - accuracy: 0.8108 - val_loss: 0.5237 - val_AUC: 0.7608 - val_accuracy: 0.7844\n",
      "Epoch 4/100\n",
      "640/666 [===========================>..] - ETA: 0s - loss: 0.4165 - AUC: 0.8081 - accuracy: 0.8375Restoring model weights from the end of the best epoch.\n",
      "666/666 [==============================] - 0s 225us/sample - loss: 0.4132 - AUC: 0.8134 - accuracy: 0.8378 - val_loss: 0.6916 - val_AUC: 0.7535 - val_accuracy: 0.5749\n",
      "Epoch 00004: early stopping\n",
      "Fold 1 fitting over.\n",
      "Fold 1 scoring over.\n",
      "Save model to:dt_output/dt_20200324 160650_dcn_nets_dnn_nets_fm_nets/dcn_nets_dnn_nets_fm_nets-kfold-1.h5.\n",
      "\n",
      "Fold:2\n",
      "\n",
      "1 Physical GPUs, 1 Logical GPUs\n",
      ">>>>>>>>>>>>>>>>>>>>>> Model Desc <<<<<<<<<<<<<<<<<<<<<<< \n",
      "---------------------------------------------------------\n",
      "inputs:\n",
      "---------------------------------------------------------\n",
      "['all_categorical_vars: (23)', 'input_continuous_all: (5)']\n",
      "---------------------------------------------------------\n",
      "embeddings:\n",
      "---------------------------------------------------------\n",
      "input_dims: [4, 4, 4, 4, 4, 5, 8, 8, 8, 6, 592, 669, 206, 194, 748, 5, 7, 8, 17, 28, 174, 9, 14]\n",
      "output_dims: [20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
      "dropout: 0.3\n",
      "---------------------------------------------------------\n",
      "dense: dropout: 0\n",
      "batch_normalization: False\n",
      "---------------------------------------------------------\n",
      "concat_embed_dense: shape: (None, 465)\n",
      "---------------------------------------------------------\n",
      "nets: ['dcn_nets', 'dnn_nets', 'fm_nets']\n",
      "---------------------------------------------------------\n",
      "dcn-widecross: input_shape (None, 465), output_shape (None, 465)\n",
      "dcn-dnn2: input_shape (None, 465), output_shape (None, 300)\n",
      "dcn: input_shape (None, 465), output_shape (None, 765)\n",
      "dnn: input_shape (None, 465), output_shape (None, 300)\n",
      "fm: input_shape (None, 23, 20), output_shape (None, 1)\n",
      "---------------------------------------------------------\n",
      "stacking_op: add\n",
      "---------------------------------------------------------\n",
      "output: activation: sigmoid, output_shape: (None, 1), use_bias: False\n",
      "loss: binary_crossentropy\n",
      "optimizer: Adam\n",
      "---------------------------------------------------------\n",
      "\n",
      "Train on 667 samples, validate on 333 samples\n",
      "Epoch 1/100\n",
      "667/667 [==============================] - 11s 17ms/sample - loss: 1.8802 - AUC: 0.5348 - accuracy: 0.4408 - val_loss: 0.5256 - val_AUC: 0.4128 - val_accuracy: 0.8168\n",
      "Epoch 2/100\n",
      "667/667 [==============================] - 0s 240us/sample - loss: 0.7496 - AUC: 0.6351 - accuracy: 0.7451 - val_loss: 0.5087 - val_AUC: 0.4711 - val_accuracy: 0.8168\n",
      "Epoch 3/100\n",
      "667/667 [==============================] - 0s 221us/sample - loss: 0.5610 - AUC: 0.7500 - accuracy: 0.8231 - val_loss: 0.4752 - val_AUC: 0.5656 - val_accuracy: 0.8168\n",
      "Epoch 4/100\n",
      "667/667 [==============================] - 0s 220us/sample - loss: 0.4758 - AUC: 0.8213 - accuracy: 0.8426 - val_loss: 0.4792 - val_AUC: 0.6058 - val_accuracy: 0.8168\n",
      "Epoch 5/100\n",
      "667/667 [==============================] - 0s 234us/sample - loss: 0.3998 - AUC: 0.8453 - accuracy: 0.8456 - val_loss: 0.5149 - val_AUC: 0.6484 - val_accuracy: 0.7748\n",
      "Epoch 6/100\n",
      "667/667 [==============================] - 0s 256us/sample - loss: 0.3693 - AUC: 0.8630 - accuracy: 0.8516 - val_loss: 0.5667 - val_AUC: 0.6811 - val_accuracy: 0.7387\n",
      "Epoch 7/100\n",
      "667/667 [==============================] - 0s 246us/sample - loss: 0.3447 - AUC: 0.8867 - accuracy: 0.8591 - val_loss: 0.5917 - val_AUC: 0.6915 - val_accuracy: 0.7027\n",
      "Epoch 8/100\n",
      "667/667 [==============================] - 0s 245us/sample - loss: 0.3410 - AUC: 0.8896 - accuracy: 0.8771 - val_loss: 0.5661 - val_AUC: 0.6971 - val_accuracy: 0.7297\n",
      "Epoch 9/100\n",
      "667/667 [==============================] - 0s 268us/sample - loss: 0.2654 - AUC: 0.9440 - accuracy: 0.9040 - val_loss: 0.5549 - val_AUC: 0.7082 - val_accuracy: 0.7477\n",
      "Epoch 10/100\n",
      "640/667 [===========================>..] - ETA: 0s - loss: 0.2092 - AUC: 0.9496 - accuracy: 0.9156Restoring model weights from the end of the best epoch.\n",
      "667/667 [==============================] - 0s 246us/sample - loss: 0.2120 - AUC: 0.9500 - accuracy: 0.9115 - val_loss: 0.5524 - val_AUC: 0.7086 - val_accuracy: 0.7297\n",
      "Epoch 00010: early stopping\n",
      "Fold 2 fitting over.\n",
      "Fold 2 scoring over.\n",
      "Save model to:dt_output/dt_20200324 160650_dcn_nets_dnn_nets_fm_nets/dcn_nets_dnn_nets_fm_nets-kfold-2.h5.\n",
      "\n",
      "Fold:3\n",
      "\n",
      "1 Physical GPUs, 1 Logical GPUs\n",
      ">>>>>>>>>>>>>>>>>>>>>> Model Desc <<<<<<<<<<<<<<<<<<<<<<< \n",
      "---------------------------------------------------------\n",
      "inputs:\n",
      "---------------------------------------------------------\n",
      "['all_categorical_vars: (23)', 'input_continuous_all: (5)']\n",
      "---------------------------------------------------------\n",
      "embeddings:\n",
      "---------------------------------------------------------\n",
      "input_dims: [4, 4, 4, 4, 4, 5, 8, 8, 8, 6, 592, 669, 206, 194, 748, 5, 7, 8, 17, 28, 174, 9, 14]\n",
      "output_dims: [20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
      "dropout: 0.3\n",
      "---------------------------------------------------------\n",
      "dense: dropout: 0\n",
      "batch_normalization: False\n",
      "---------------------------------------------------------\n",
      "concat_embed_dense: shape: (None, 465)\n",
      "---------------------------------------------------------\n",
      "nets: ['dcn_nets', 'dnn_nets', 'fm_nets']\n",
      "---------------------------------------------------------\n",
      "dcn-widecross: input_shape (None, 465), output_shape (None, 465)\n",
      "dcn-dnn2: input_shape (None, 465), output_shape (None, 300)\n",
      "dcn: input_shape (None, 465), output_shape (None, 765)\n",
      "dnn: input_shape (None, 465), output_shape (None, 300)\n",
      "fm: input_shape (None, 23, 20), output_shape (None, 1)\n",
      "---------------------------------------------------------\n",
      "stacking_op: add\n",
      "---------------------------------------------------------\n",
      "output: activation: sigmoid, output_shape: (None, 1), use_bias: False\n",
      "loss: binary_crossentropy\n",
      "optimizer: Adam\n",
      "---------------------------------------------------------\n",
      "\n",
      "Train on 667 samples, validate on 333 samples\n",
      "Epoch 1/100\n",
      "667/667 [==============================] - 10s 15ms/sample - loss: 1.0725 - AUC: 0.5234 - accuracy: 0.5622 - val_loss: 0.6949 - val_AUC: 0.4349 - val_accuracy: 0.7387\n",
      "Epoch 2/100\n",
      "667/667 [==============================] - 0s 241us/sample - loss: 0.5763 - AUC: 0.6425 - accuracy: 0.7721 - val_loss: 0.5582 - val_AUC: 0.4851 - val_accuracy: 0.8048\n",
      "Epoch 3/100\n",
      "667/667 [==============================] - 0s 235us/sample - loss: 0.5136 - AUC: 0.7593 - accuracy: 0.8231 - val_loss: 0.5259 - val_AUC: 0.5672 - val_accuracy: 0.8108\n",
      "Epoch 4/100\n",
      "667/667 [==============================] - 0s 244us/sample - loss: 0.4556 - AUC: 0.7834 - accuracy: 0.8321 - val_loss: 0.5672 - val_AUC: 0.6663 - val_accuracy: 0.7538\n",
      "Epoch 5/100\n",
      "667/667 [==============================] - 0s 242us/sample - loss: 0.3745 - AUC: 0.8327 - accuracy: 0.8651 - val_loss: 0.6833 - val_AUC: 0.6944 - val_accuracy: 0.5856\n",
      "Epoch 6/100\n",
      "667/667 [==============================] - 0s 230us/sample - loss: 0.3477 - AUC: 0.8585 - accuracy: 0.8516 - val_loss: 0.7557 - val_AUC: 0.7014 - val_accuracy: 0.4715\n",
      "Epoch 7/100\n",
      "640/667 [===========================>..] - ETA: 0s - loss: 0.2531 - AUC: 0.9287 - accuracy: 0.8813Restoring model weights from the end of the best epoch.\n",
      "667/667 [==============================] - 0s 232us/sample - loss: 0.2553 - AUC: 0.9271 - accuracy: 0.8801 - val_loss: 0.7405 - val_AUC: 0.6991 - val_accuracy: 0.4835\n",
      "Epoch 00007: early stopping\n",
      "Fold 3 fitting over.\n",
      "Fold 3 scoring over.\n",
      "Save model to:dt_output/dt_20200324 160650_dcn_nets_dnn_nets_fm_nets/dcn_nets_dnn_nets_fm_nets-kfold-3.h5.\n",
      "fit_cross_validation cost:41.44494962692261\n",
      "CPU times: user 44.6 s, sys: 2.61 s, total: 47.2 s\n",
      "Wall time: 41.5 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:   41.3s finished\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "conf = ModelConfig(\n",
    "    dnn_params={\n",
    "        'hidden_units':((300, 0.3, True),(300, 0.3, True),), #hidden_units\n",
    "        'dnn_activation':'relu',\n",
    "    },\n",
    "    fixed_embedding_dim=True, \n",
    "    embeddings_output_dim=20, \n",
    "    nets =['fm_nets','dcn_nets','dnn_nets'],\n",
    "    output_use_bias = False,\n",
    "    cross_params={\n",
    "        'num_cross_layer': 4,\n",
    "    },\n",
    ")\n",
    "\n",
    "dt = DeepTable(config = conf)\n",
    "oof_proba, eval_proba, test_prob  = dt.fit_cross_validation(\n",
    "    X, y, X_eval=None, X_test=None, \n",
    "    num_folds=n_folds, stratified=False, iterators=None,\n",
    "    batch_size=batch_size, epochs=epochs, verbose=1, callbacks=[], n_jobs=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Output submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission['target'] = test_prob\n",
    "submission.to_csv(f'submission_fm_dcn_dnn_kfold50.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load model from disk:dt_output/dt_20200324 160650_dcn_nets_dnn_nets_fm_nets/dcn_nets_dnn_nets_fm_nets-kfold-3.h5.\n",
      "1 Physical GPUs, 1 Logical GPUs\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_model(dt.get_model().model,rankdir='TB')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start cross validation\n",
      "2 class detected, {0, 1}, so inferred as a [binary classification] task\n",
      "Preparing features cost:0.02204585075378418\n",
      "Imputation cost:0.04611682891845703\n",
      "Categorical encoding cost:0.05957388877868652\n",
      "fit_transform cost:0.13257122039794922\n",
      "Iterators:KFold(n_splits=3, random_state=9527, shuffle=True)\n",
      "Injected a callback [EarlyStopping]. monitor:val_AUC, patience:1, mode:max\n",
      "\n",
      "Fold:1\n",
      "\n",
      "1 Physical GPUs, 1 Logical GPUs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>>>>>>>>>>>>>>>>>>>>> Model Desc <<<<<<<<<<<<<<<<<<<<<<< \n",
      "---------------------------------------------------------\n",
      "inputs:\n",
      "---------------------------------------------------------\n",
      "['all_categorical_vars: (23)', 'input_continuous_all: (5)']\n",
      "---------------------------------------------------------\n",
      "embeddings:\n",
      "---------------------------------------------------------\n",
      "input_dims: [4, 4, 4, 4, 4, 5, 8, 8, 8, 6, 592, 669, 206, 194, 748, 5, 7, 8, 17, 28, 174, 9, 14]\n",
      "output_dims: [20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
      "dropout: 0.3\n",
      "---------------------------------------------------------\n",
      "dense: dropout: 0\n",
      "batch_normalization: False\n",
      "---------------------------------------------------------\n",
      "concat_embed_dense: shape: (None, 465)\n",
      "---------------------------------------------------------\n",
      "nets: ['opnn_nets', 'fm_nets', 'cross_nets']\n",
      "---------------------------------------------------------\n",
      "opnn-outer_product: input_shape list(23), output_shape (None, 253)\n",
      "opnn-dnn: input_shape (None, 718), output_shape (None, 300)\n",
      "fm: input_shape (None, 23, 20), output_shape (None, 1)\n",
      "cross: input_shape (None, 465), output_shape (None, 465)\n",
      "---------------------------------------------------------\n",
      "stacking_op: add\n",
      "---------------------------------------------------------\n",
      "output: activation: sigmoid, output_shape: (None, 1), use_bias: False\n",
      "loss: binary_crossentropy\n",
      "optimizer: Adam\n",
      "---------------------------------------------------------\n",
      "\n",
      "Train on 666 samples, validate on 334 samples\n",
      "Epoch 1/100\n",
      "666/666 [==============================] - 9s 14ms/sample - loss: 1.3088 - AUC: 0.5007 - accuracy: 0.5015 - val_loss: 0.5111 - val_AUC: 0.4823 - val_accuracy: 0.8174\n",
      "Epoch 2/100\n",
      "666/666 [==============================] - 0s 265us/sample - loss: 0.8037 - AUC: 0.5714 - accuracy: 0.7447 - val_loss: 0.4534 - val_AUC: 0.5115 - val_accuracy: 0.8323\n",
      "Epoch 3/100\n",
      "666/666 [==============================] - 0s 229us/sample - loss: 0.5421 - AUC: 0.7090 - accuracy: 0.7973 - val_loss: 0.4440 - val_AUC: 0.6123 - val_accuracy: 0.8323\n",
      "Epoch 4/100\n",
      "666/666 [==============================] - 0s 240us/sample - loss: 0.4839 - AUC: 0.7640 - accuracy: 0.8378 - val_loss: 0.4553 - val_AUC: 0.6692 - val_accuracy: 0.8323\n",
      "Epoch 5/100\n",
      "666/666 [==============================] - 0s 242us/sample - loss: 0.5121 - AUC: 0.7933 - accuracy: 0.8213 - val_loss: 0.5000 - val_AUC: 0.6805 - val_accuracy: 0.8234\n",
      "Epoch 6/100\n",
      "640/666 [===========================>..] - ETA: 0s - loss: 0.4273 - AUC: 0.8386 - accuracy: 0.8391Restoring model weights from the end of the best epoch.\n",
      "666/666 [==============================] - 0s 252us/sample - loss: 0.4263 - AUC: 0.8378 - accuracy: 0.8378 - val_loss: 0.5485 - val_AUC: 0.6789 - val_accuracy: 0.7754\n",
      "Epoch 00006: early stopping\n",
      "Fold 1 fitting over.\n",
      "Fold 1 scoring over.\n",
      "Save model to:dt_output/dt_20200324 160746_opnn_nets_fm_nets_cross_nets/opnn_nets_fm_nets_cross_nets-kfold-1.h5.\n",
      "\n",
      "Fold:2\n",
      "\n",
      "1 Physical GPUs, 1 Logical GPUs\n",
      ">>>>>>>>>>>>>>>>>>>>>> Model Desc <<<<<<<<<<<<<<<<<<<<<<< \n",
      "---------------------------------------------------------\n",
      "inputs:\n",
      "---------------------------------------------------------\n",
      "['all_categorical_vars: (23)', 'input_continuous_all: (5)']\n",
      "---------------------------------------------------------\n",
      "embeddings:\n",
      "---------------------------------------------------------\n",
      "input_dims: [4, 4, 4, 4, 4, 5, 8, 8, 8, 6, 592, 669, 206, 194, 748, 5, 7, 8, 17, 28, 174, 9, 14]\n",
      "output_dims: [20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
      "dropout: 0.3\n",
      "---------------------------------------------------------\n",
      "dense: dropout: 0\n",
      "batch_normalization: False\n",
      "---------------------------------------------------------\n",
      "concat_embed_dense: shape: (None, 465)\n",
      "---------------------------------------------------------\n",
      "nets: ['opnn_nets', 'fm_nets', 'cross_nets']\n",
      "---------------------------------------------------------\n",
      "opnn-outer_product: input_shape list(23), output_shape (None, 253)\n",
      "opnn-dnn: input_shape (None, 718), output_shape (None, 300)\n",
      "fm: input_shape (None, 23, 20), output_shape (None, 1)\n",
      "cross: input_shape (None, 465), output_shape (None, 465)\n",
      "---------------------------------------------------------\n",
      "stacking_op: add\n",
      "---------------------------------------------------------\n",
      "output: activation: sigmoid, output_shape: (None, 1), use_bias: False\n",
      "loss: binary_crossentropy\n",
      "optimizer: Adam\n",
      "---------------------------------------------------------\n",
      "\n",
      "Train on 667 samples, validate on 333 samples\n",
      "Epoch 1/100\n",
      "667/667 [==============================] - 11s 16ms/sample - loss: 1.4748 - AUC: 0.5076 - accuracy: 0.5757 - val_loss: 0.5320 - val_AUC: 0.6659 - val_accuracy: 0.7748\n",
      "Epoch 2/100\n",
      "512/667 [======================>.......] - ETA: 0s - loss: 0.7451 - AUC: 0.6483 - accuracy: 0.7773Restoring model weights from the end of the best epoch.\n",
      "667/667 [==============================] - 0s 253us/sample - loss: 0.7219 - AUC: 0.6425 - accuracy: 0.7811 - val_loss: 0.4627 - val_AUC: 0.6583 - val_accuracy: 0.8198\n",
      "Epoch 00002: early stopping\n",
      "Fold 2 fitting over.\n",
      "Fold 2 scoring over.\n",
      "Save model to:dt_output/dt_20200324 160746_opnn_nets_fm_nets_cross_nets/opnn_nets_fm_nets_cross_nets-kfold-2.h5.\n",
      "\n",
      "Fold:3\n",
      "\n",
      "1 Physical GPUs, 1 Logical GPUs\n",
      ">>>>>>>>>>>>>>>>>>>>>> Model Desc <<<<<<<<<<<<<<<<<<<<<<< \n",
      "---------------------------------------------------------\n",
      "inputs:\n",
      "---------------------------------------------------------\n",
      "['all_categorical_vars: (23)', 'input_continuous_all: (5)']\n",
      "---------------------------------------------------------\n",
      "embeddings:\n",
      "---------------------------------------------------------\n",
      "input_dims: [4, 4, 4, 4, 4, 5, 8, 8, 8, 6, 592, 669, 206, 194, 748, 5, 7, 8, 17, 28, 174, 9, 14]\n",
      "output_dims: [20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
      "dropout: 0.3\n",
      "---------------------------------------------------------\n",
      "dense: dropout: 0\n",
      "batch_normalization: False\n",
      "---------------------------------------------------------\n",
      "concat_embed_dense: shape: (None, 465)\n",
      "---------------------------------------------------------\n",
      "nets: ['opnn_nets', 'fm_nets', 'cross_nets']\n",
      "---------------------------------------------------------\n",
      "opnn-outer_product: input_shape list(23), output_shape (None, 253)\n",
      "opnn-dnn: input_shape (None, 718), output_shape (None, 300)\n",
      "fm: input_shape (None, 23, 20), output_shape (None, 1)\n",
      "cross: input_shape (None, 465), output_shape (None, 465)\n",
      "---------------------------------------------------------\n",
      "stacking_op: add\n",
      "---------------------------------------------------------\n",
      "output: activation: sigmoid, output_shape: (None, 1), use_bias: False\n",
      "loss: binary_crossentropy\n",
      "optimizer: Adam\n",
      "---------------------------------------------------------\n",
      "\n",
      "Train on 667 samples, validate on 333 samples\n",
      "Epoch 1/100\n",
      "667/667 [==============================] - 9s 13ms/sample - loss: 0.6632 - AUC: 0.5327 - accuracy: 0.6822 - val_loss: 0.8922 - val_AUC: 0.6692 - val_accuracy: 0.4685\n",
      "Epoch 2/100\n",
      "667/667 [==============================] - 0s 233us/sample - loss: 0.5265 - AUC: 0.6489 - accuracy: 0.8141 - val_loss: 0.7028 - val_AUC: 0.6831 - val_accuracy: 0.6156\n",
      "Epoch 3/100\n",
      "640/667 [===========================>..] - ETA: 0s - loss: 0.4481 - AUC: 0.7598 - accuracy: 0.8203Restoring model weights from the end of the best epoch.\n",
      "667/667 [==============================] - 0s 691us/sample - loss: 0.4477 - AUC: 0.7580 - accuracy: 0.8201 - val_loss: 0.6297 - val_AUC: 0.6826 - val_accuracy: 0.7267\n",
      "Epoch 00003: early stopping\n",
      "Fold 3 fitting over.\n",
      "Fold 3 scoring over.\n",
      "Save model to:dt_output/dt_20200324 160746_opnn_nets_fm_nets_cross_nets/opnn_nets_fm_nets_cross_nets-kfold-3.h5.\n",
      "fit_cross_validation cost:37.83621120452881\n",
      "CPU times: user 39.3 s, sys: 1.56 s, total: 40.9 s\n",
      "Wall time: 37.8 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:   37.7s finished\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "conf = ModelConfig(\n",
    "    dnn_params={\n",
    "        'hidden_units':((300, 0.3, True),(300, 0.3, True),), \n",
    "        'dnn_activation':'relu',\n",
    "    },\n",
    "    fixed_embedding_dim=True, \n",
    "    embeddings_output_dim=20, \n",
    "     auto_discrete=False,\n",
    "     auto_categorization=False,\n",
    "    nets =['fm_nets','opnn_nets','cross_nets'],\n",
    "    output_use_bias = False,\n",
    "    pnn_params={\n",
    "        'outer_product_kernel_type': 'mat',\n",
    "    },    \n",
    "    cross_params={\n",
    "        'num_cross_layer': 4,\n",
    "    },\n",
    ")\n",
    "\n",
    "dt = DeepTable(config = conf)\n",
    "oof_proba, eval_proba, test_prob  = dt.fit_cross_validation(\n",
    "    X, y, X_eval=None, X_test=None, \n",
    "    num_folds=n_folds, stratified=False, iterators=None,\n",
    "    batch_size=batch_size, epochs=epochs, verbose=1, callbacks=[], n_jobs=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Output submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission['target'] = test_prob\n",
    "submission.to_csv(f'submission_fm_opnn_cross_kfold50.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load model from disk:dt_output/dt_20200324 160746_opnn_nets_fm_nets_cross_nets/opnn_nets_fm_nets_cross_nets-kfold-3.h5.\n",
      "1 Physical GPUs, 1 Logical GPUs\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_model(dt.get_model().model,rankdir='TB')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
